A Dissertation
Presented to the Faculty of the Graduate School
of Cornell University
in Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy
Bo Pang
August 2006
c 2006 Bo Pang
Bo Pang, Ph.D.
Cornell University 2006
Sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has attracted a great deal of attention. Potential
applications include question-answering systems that address opinions as opposed
to facts and business intelligence systems that analyze user feedback. The research
issues raised by such applications are often quite challenging compared to factbased analysis. This thesis presents several sentiment analysis tasks to illustrate
the new challenges and opportunities. In particular, we describe how we modeled
different types of relations in approaching several sentiment analysis problems; our
models can have implications outside this area as well.
One task is polarity classification, where we classify a movie review as “thumbs
up” or “thumbs down” from textual information alone. We consider a number
of approaches, including one that applies text categorization techniques to just
the subjective portions of the document. Extracting these portions can be a hard
problem in itself; we describe an approach based on efficient techniques for finding minimum cuts in graphs that incorporate sentence-level relations. The second
task, which can be viewed as a non-standard multi-class classification task, is the
rating-inference problem, where one must determine the reviewer’s evaluation with
respect to a multi-point scale (e.g. one to five “stars”). We apply a meta-algorithm,
based on a metric-labeling formulation of the problem, that explicitly exploits relations between classes. A different type of relationship between text units is
considered in the third task, where we investigate whether one can determine from
the transcripts of U.S. Congressional floor debates whether the speeches represent
support of or opposition to proposed legislation. In particular, we exploit the fact
that these speeches occur as part of a discussion. We find that the incorporation
of information regarding relationships between discourse segments yields substantial improvements over classifying speeches in isolation. Lastly, we introduce and
discuss a sentiment analysis problem arising in Web-search applications: given
documents on the same focused topic, we wish to rank the subjective documents
before objective ones. We present early results with unsupervised approaches that
do not assume prior linguistic or domain-specific knowledge.
Bo Pang received her B.S. degree in Computer Science from Tsinghua University.
She is currently a Ph.D. student in Computer Science at Cornell University.
I am eternally grateful to Lillian Lee, who has been an amazing advisor in
so many aspects of this role. Apart from numerous other things (both researchand non-research-wise) that I have learned from her, it was her introduction to
statistical methods and models that got me interested in NLP in the first place.
Thanks to her support, patience, sense of humor, and consistently wise advice, I
have greatly enjoyed working in this field. On top of all these, what touches me the
most is that she truly cares1 ; for this alone I can never thank her enough. Shimon
Edelman, my minor advisor, has been a great resource. I learned much of what
I know about cognitive science from him; and during bad times in my research,
attending his lectures and seminars restored in me a sense of being intellectually
alive. Many thanks also to the other members on my committee — Claire Cardie,
Thorsten Joachims, and Eva Tardos, for many helpful discussions and feedback,
as well as constant encouragement and valuable advice.
The work described in this thesis undoubtedly benefited from discussions with
my committee members; in addition, I would like to also thank Paul Bennett, Dave
Blei, Eric Breck, Rich Caruana, Yejin Choi, Dan Huttenlocher, Jon Kleinberg,
Oren Kurland, John Lafferty, Guy Lebanon, Michael Macy, Art Munson, Andrew
Myers, Vincent Ng, Fernando Pereira, Pradeep Ravikumar, Phoebe Sengers, Ves
Stoyanov, David Strang, Richard Tong, Peter Turney, Ramin Zabih, Jerry Zhu,
and the anonymous reviewers for many valuable comments and helpful suggestions.
Portions of this work were started while I was visiting IBM Almaden as a
summer intern. I really appreciate all the help and input I received from my
mentor Shivakumar Vaithyanathan. ISI is undoubtedly a wonderful place to do
I hope she won’t be rolling her eyes at this with “what is this? ‘vote for Lillian; she cares?’ ” —
although quite likely she will.
NLP research. Many thanks to my mentors, Kevin Knight and Daniel Marcus,
for their insights and advice. I also greatly enjoyed my interactions with the NLP
group there, and especially want to thank Hal Daumé III, Ulrich Germann, Ulf
Hermjakob, and Radu Soricut for help and discussions. For yet another happy
summer in California and valuable experience with the “real world”, I thank my
host at Google, Xuefu Wang. I also thank CMU for their hospitality during my
one-year visit; it has been an extremely enriching experience for me.
The computer science department in Cornell has been a most supportive and
friendly community. It is indeed an impossible task to name all the people that
have helped and influenced me over the years and to thank them individually. I
am especially thankful to the NLP group and the machine learning people. I have
always enjoyed both academic and casual conversations at the NLP reading group,
AI lunch, and MLDG (thanks to Eric Breck, Alexandru Niculescu-Mizil, and Filip
Radlinski for putting so much thought into it; you can now, once again, enjoy
being the most senior students!); as well as Friday beers at CTB and Big Red
Barn. I also enjoyed many inspiring conversations with Regina Barzilay when she
visited Cornell. Her enthusiasm has always put a positive spin on me. Another
positive force was our resourceful Cindy Robinson; many thanks to her for her
help, laughters, and candies. I have been blessed with many great officemates
throughout the years. I thank Greg Bronevetsky, Hubie Chen, Tony Faradjian,
Dan Kifer, Oren Kurland, Xiangyang Lan, and Yong Yao (and Elliot Anshelevich
and Leonid Meyerguz, whose visits to 4107 were at least partially responsible for
our noise level!) for making the graduate school years so much easier.
Life would have been much different without the “dinner gang” — Hailing,
Lin, Tina, Yanling, Min and Min at the “Ithaca headquarter”, as well as Ting and
Minglong, when I was in Pittsburgh. Thanks for all the happy dinners and countless indecisive hours we wasted together; thanks also for tolerating my occasional
outburst of “artistic impulse” and my not so occasional urge to argue.
Last but not least, deepest thanks to my parents, who have always stood behind
me for who I am. Their trust and support have kept me grounded, and made me
believe in myself when I couldn’t find any reasons to.
This thesis is based upon work supported in part by the National Science
Foundation under grant no. IIS-0081334, IIS-0329064, and CCR-0122581; SRI
International under subcontract no. 03-000211 on their project funded by the Department of the Interior’s National Business Center; a Cornell Graduate Fellowship
in Cognitive Studies; Google Anita Borg Memorial Scholarship funds; and by an
Alfred P. Sloan Research Fellowship.
1 Introduction
2 Related work
2.1 Related work in sentiment analysis . . . . . . . . . . . . . . . . . .
2.2 Other non-factual information in text . . . . . . . . . . . . . . . . .
3 Polarity classification
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . .
3.1.1 Movie Review Data . . . . . . . . . . . . . . .
3.2 Polarity Classification via Machine Learning Methods
3.2.1 A Closer Look At the Problem . . . . . . . .
3.2.2 Machine Learning Methods . . . . . . . . . .
3.2.3 Evaluation . . . . . . . . . . . . . . . . . . . .
3.2.4 Discussion . . . . . . . . . . . . . . . . . . . .
3.3 Polarity classification with subjective summaries . . .
3.3.1 Architecture . . . . . . . . . . . . . . . . . . .
3.3.2 Context and Subjectivity Detection . . . . . .
3.3.3 Cut-based classification . . . . . . . . . . . . .
3.3.4 Related work for the graph-cut formulation . .
3.3.5 Evaluation Framework . . . . . . . . . . . . .
3.3.6 Experimental Results . . . . . . . . . . . . . .
3.3.7 Classification based on hidden Markov models
3.3.8 Conclusions . . . . . . . . . . . . . . . . . . .
4 Exploiting class relationships for sentiment categorization with
respect to rating scales
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Problem validation and formulation . . . . . . . . . . . . . . . . . .
4.3 Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.1 One-vs-all . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.2 Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.3 Metric labeling . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 Class struggle: finding a label-correlated item-similarity function . .
4.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5.1 Main comparison . . . . . . . . . . . . . . . . . . . . . . . .
4.5.2 Further discussion . . . . . . . . . . . . . . . . . . . . . . . .
4.6 Related work and future directions . . . . . . . . . . . . . . . . . .
4.7 Other variations attempted . . . . . . . . . . . . . . . . . . . . . .
4.7.1 Discretizing binary classification . . . . . . . . . . . . . . . .
4.7.2 Discretizing regression . . . . . . . . . . . . . . . . . . . . .
5 Variation on the theme of polarity and document relationship
with politically oriented text
5.1 Introduction: determining support or opposition from Congressional
floor-debate transcripts . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Corpus: transcripts of U.S. floor debates . . . . . . . . . . . . . . .
5.3 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3.1 Classifying speech segments in isolation . . . . . . . . . . . .
5.3.2 Relationships between speech segments . . . . . . . . . . . .
5.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.4.1 Preliminaries: Reference classification . . . . . . . . . . . . .
5.4.2 Segment-based speech-segment classification . . . . . . . . .
5.4.3 Speaker-based speech-segment classification . . . . . . . . .
5.4.4 “Hard” agreement constraints . . . . . . . . . . . . . . . . .
5.4.5 On the development/test set split . . . . . . . . . . . . . . .
5.5 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.6 Conclusion and future work . . . . . . . . . . . . . . . . . . . . . .
6 Rhapsody on the theme of subjectivity: collective document-level
subjectivity detection without training data
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
6.2 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
6.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
6.4 Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
6.4.1 Stopword removal . . . . . . . . . . . . . . . . . . . . . . . . 100
6.4.2 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
6.4.3 Idiosyncrasy-based approaches . . . . . . . . . . . . . . . . . 101
6.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
6.5.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . 103
6.5.2 Baselines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
6.5.3 Comparison of individual algorithms . . . . . . . . . . . . . 105
6.5.4 Hybrid algorithms . . . . . . . . . . . . . . . . . . . . . . . 108
6.6 Conclusions and Future work . . . . . . . . . . . . . . . . . . . . . 109
7 Unanswered questions
Average accuracies for different machine learning methods on the
polarity classification task . . . . . . . . . . . . . . . . . . . . . . .
Human accuracy at determining relative positivity. . . . . . . . . .
Average over authors and class pairs of between-class vocabulary
overlap as the class labels of the pair grow farther apart. . . . . .
Statistics for the congressional debate dataset . . . . .
Agreement-classifier accuracy . . . . . . . . . . . . . .
Agreement-classifier precision. . . . . . . . . . . . . .
Segment-based speech-segment classification accuracy
Speaker-based speech-segment classification accuracy .
Results with hard agreement constraints . . . . . . . .
Statistics for the search-set corpus . . . . . . . . . . . . . . . . . . 99
Average test-set results for objective-document precision at the
number of objective documents. . . . . . . . . . . . . . . . . . . . 105
(Objective) precision and recall of the presumably objective cluster. 106
Reviews on Cornell University from . . . . . . . . .
Baseline results for human word lists (I) . . . . . . . . . . . . . . .
Baselines using introspection and simple statistics of the test data.
Polarity classification via subjectivity detection. . . . . . . . . . .
Graph for classifying three items with the minimum cut framework
Graph-cut-based creation of subjective extracts. . . . . . . . . . .
Accuracies using N-sentence extracts for NB and SVM default polarity classifiers. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Word preservation rate vs. accuracy, NB and SVMs as default
polarity classifiers. . . . . . . . . . . . . . . . . . . . . . . . . . .
Average and standard deviation of PSP for reviews expressing different ratings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Main experimental comparisons for classification with respect to
rating scales: three-class classification results . . . . . . . . . . . .
Main experimental comparisons for classification with respect to
rating scales: four-class classification results . . . . . . . . . . . . .
Chapter 1
Will, you must forgive me, but I have not the slightest sympathy with
what the world calls Sentiment – not the slightest.
— Mark Twain, Letter to Will Bowen
A large portion of research in natural language processing seeks to better understand and process various types of information in text. In the past, the majority of
such work focused on factual information: work in text categorization largely concentrated on classifying documents according to their subject matter (e.g., politics
vs. sports); question answering systems aimed to answer fact-oriented questions
such as “who did what, when and where”; similarly, research in text summarization
mainly focused on collections of news articles where it is important to “keep the
facts straight”. Indeed, text segments such as “Cornell is located in Ithaca, NY”
contain nothing but facts. But if we look at the examples shown in Figure 1.1,
although it is useful to detect factual information such as both text segments are
about Cornell University, it is just as useful (and perhaps even more interesting)
to understand the author’s sentiment indicated by the text.
Quoting the first two senses given by the Merriam-Webster Online Dictionary,
Excerpt of Review 1 (by pandora)
School, Friends, Sleep. Choose 2.
Mar 27 ’00
Pros Beautiful campus
Cons everything else
Ahhh? Fair Cornell University? High above Cayuga’s waters.
And pretty much that is all that is good about Cornell University.
Excerpt of Review 2 (by heafdog)
I would cut off a finger to do it all again.
Aug 16 ’01
Pros Beautiful campus, excellent size, terrific experience.
Cons Can’t think of any.
When I say that I would cut off a finger to do it all over again, it sounds
sort of drastic, but I really mean that. I can go on and on for hours about
how much I loved Cornell. It was the best four years of my life, hands
down. In the evening after my graduation from there, back in 1998, I
was so sad to be leaving there that I cried my eyes out. Seriously.
Figure 1.1: Reviews on Cornell University from
the word sentiment can be used to indicate
1. a: an attitude, thought, or judgment prompted by feeling : PREDILECTION
b: a specific view or notion : OPINION
2. a: EMOTION b: refined feeling : delicate sensibility especially as expressed
in a work of art
c: emotional idealism
d: a romantic or nostalgic feeling
verging on sentimentality
It is the first sense (“attitude, judgment, and view”) of sentiment, rather than
the “delicate feeling” or “nostalgic feeling verging on sentimentality”1 , that we
attempt to address in this work. In particular, we focus on the computational
analysis of such information in text.
For instance, one salient feature of the texts shown in Figure 1.1 is the sentiment polarity. The first review clearly indicates a negative opinion towards Cornell,
whereas the second one, in spite of associating Cornell with the (presumably negative) image of cutting off a finger, conveys positive sentiment. While it is fairly
easy for human readers to determine the sentiment polarity of these two examples,
it is quite a challenge for machines to accomplish the same task automatically. As
we know, most text classification methods (i.e., techniques that classify a given
document into one of the pre-defined classes) are based on “bag of words” models,
where each document is represented by the set of words it contains, ignoring the
original order in which these words appear. Simple as it is, looking at the statistics of the constituent words alone has proven quite effective for topic-based text
classification — after all, it is quite reasonable to assume that texts about Cornell
are more likely to contain certain keywords (such as, obviously, “Cornell”, or, not
so obviously, “Cayuga”). In contrast, in the case of sentiment-based classification,
while the positive review in Figure 1.1 does contain “positive” keywords such as
“loved” and “best”, the negative review contains no apparent “negative” words.
In fact, plenty of “positive” words such as “beautiful” and “fair” are used in this
It seems it is this second sense of “sentiment” that Mark Twain does not have sympathy with.
negative example. In general, while topics (e.g., politics or sports) are often identifiable by keywords alone, sentiment can be expressed in a more subtle manner
and seems to require deeper analysis. We will discuss this particular problem in
more detail in Chapter 3. It suffices to say that compared to traditional fact-based
analysis, sentiment-oriented text analysis problems bear unique characteristics and
present us with interesting new challenges.
Indeed, sentiment analysis, or the computational treatment of opinion, sentiment, and subjectivity, has recently attracted a great deal of attention (see Chapter
2 for a survey on related work). In addition to the research challenges, the growing
interest in this area is also partly due to the many potential applications. The
texts shown in Figure 1.1 are not two isolated examples dug out from a singular
site dedicated to online reviews. Recent years have seen rapid growth in various
kinds of on-line discussion groups and review sites, such as the New York Times
Books web page or the Internet Movie Database (IMDb), where a crucial characteristic of the messages posted is their sentiment, or overall opinion towards
the subject matter (e.g., whether a product review is positive or negative, as we
just discussed above). Labeling such messages with their sentiment would provide
succinct summaries to readers; indeed, these labels are part of the appeal and
value-add of sites like, which both labels movie reviews
that do not contain explicit rating indicators and normalizes the different rating
schemes of many reviewers. Similarly, many other interesting applications can
benefit from automatic analysis of sentiment in verbal contexts: business intelligence applications can use summaries of user feedback; recommender systems (e.g.
Terveen et al. (1997)) may attain better user profiles by analyzing textual comments; information-extraction and question-answering systems can be enhanced to
deal with statements and queries regarding opinions rather than facts (Cardie et
al., 2003). Potential applications also include message filtering, in that perhaps
one could use sentiment information to recognize and discard “flames”(Spertus,
In addition to the interesting characteristics of sentiment analysis problems, we
also investigate the modeling of relations in text collections. The data commonly
used in natural language processing (NLP) research in the past tended to be clean,
edited, and grammatical, as they were dominated by collections of text published
by traditional media in the form of books or newspapers. Nowadays, much of the
textual data available on the web, such as blog postings or user reviews, tends
to be less structured and sometimes even less well-formed, containing incorrect
spellings or grammar mistakes. While such “flawed” data may be more resistant
to traditional NLP systems such as full parsers, there is still interesting “beyondbag-of-words” information to explore. In particular, various types of relations exist
at different levels of text collections. For instance, at the sentence level, proximity
can be an interesting relation to model since nearby sentences in a document
may share certain properties we are interested in. One focus of this work is the
mathematical modeling of such relations within machine learning approaches. We
believe the techniques we explored are not unique to sentiment analysis and should
have broader implications.
The majority of the work described in this thesis focuses on classification tasks.
Most of the problems are investigated in the context of classifying a given opinionbearing document (e.g., a review or the transcript of a political speech) according to
the sentiment polarity (a two-class problem) or degree of positivity (a multi-class
problem) expressed in the text. A form of sentiment summarization is consid-
ered in this context by extracting the subjective portions of an input document,
touching upon the problem of subjectivity detection We also examine a ranking
problem with the goal of identifying opinion-bearing documents in a web-search
setting, where we experimented with completely unsupervised approaches. Thus,
this thesis is organized as follows. Chapter 2 gives a brief survey on related work;
Chapters 3, 4, and 5 consider several sentiment-oriented classification problems,
with a particular emphasis on modeling relations; Chapter 6 describes our preliminary study of the ranking problem in the sentiment context; and we conclude with
Chapter 7.
Contributions: This thesis describes explorations of several sentiment-analysis
problems from a purely data-driven perspective. We do believe deeper linguistic
knowledge can further improve the performance, and it is important to note that
many methods described in this thesis can easily incorporate prior knowledge,
complementing other work with more focus on feature engineering or linguistic
knowledge. But our main focus in this work was to explore how much can be
learned from the raw data. In fact, it can be surprising how much machine learning
approaches can achieve on seemingly sophisticated problems like sentiment polarity
Whenever possible, we tried to carry out pilot studies to take closer looks at
the problem being considered before exploring more sophisticated methods. This
way, we gain a better understanding of the actual difficulty of the problem in our
specific setting, rather than trusting our intuition, or prior belief.
This thesis also examines non-labor-intensive ways to collect interesting datasets.
The corpus we constructed range from collections of movie reviews to transcripts
political debates, with labels extracted automatically from online resources. These
datasets enable as well as motivate the problems explored in this thesis, serving as
the basis for our experiments with machine-learning methods.
We show that incorporating various types of relationship information through
conceptually simple, yet flexible frameworks can provide significant improvements
over relation-blind methods. In particular, we describe an approach based on
efficient techniques for finding minimum cuts in graphs to incorporate sentencelevel relations within a given document as well as document-level relations between
speeches that occur as part of a discussion. We also consider a meta-algorithm,
based on a metric-labeling formulation, that explicitly exploits relations between
classes for a multi-class classification problem.
Chapter 2
Related work
There has recently been a dramatic surge of interest in sentiment analysis, as more
and more people become aware of the scientific challenges posed and the scope
of new applications enabled by the processing of subjective language. The papers
collected by Qu, Shanahan, and Wiebe (2004) form a relatively early representative
sample of research in the area; and Esuli (2006) maintains an active bibliography.
Section 2.1 briefly surveys a range of different sentiment analysis tasks that formed
the “environment” within which the work described in this thesis was developed.
Section 2.2 discusses other work that investigated non-factual information in text.
We leave the discussion of related work that is less focused on sentiment to later
chapters as it is more relevant to other aspects of the specific problems we consider
in this work.
Related work in sentiment analysis
Polarity classification As mentioned earlier, the task of sentiment polarity classification, where we want to classify an opinionated document as either positive or
negative according to the sentiment expressed by the author, has attracted a lot
of interest in past years. (Note that the rating-inference problem, where one must
determine the reviewer’s evaluation with respect to a multi-point scale (e.g. one to
five “stars”) can be viewed as a multi-class version of this problem. See Chapter
4 for related work specific to that problem.)
Early work on sentiment-based categorization of entire documents has often
involved either the use of models inspired by cognitive linguistics (Hearst, 1992;
Sack, 1994) or the manual or semi-manual construction of discriminant-word lexicons (Huettner and Subasic, 2000; Das and Chen, 2001; Tong, 2001). For instance,
Das and Chen (2001) presented a methodology for real time sentiment extraction
in the domain of finance; working with messages from web-based stock message
boards, attempt to automatically label each such message as a “buy”, “sell” or
“neutral” recommendation. Their classifier achieves an accuracy of 62% (the upper bound, human agreement rate, was 72%). Unfortunately, their method entails
constructing a discriminant-word lexicon by manual selection and tagging of words
from several thousand messages. This is something we wish to avoid in our work.
In contrast, with (automatically) labeled data collected from the web, we approached the related task of determining sentiment polarity in reviews via supervised learning approaches (see Chapter 3 for more detail). Interestingly, our
baseline experiments on this task, described in Section 3.2.1, show that humans
may not always have the best intuition for choosing discriminating words. While
we did experiment with a set of different features in our early work (see Pang et
al. (2002), or Section 3.2), as we pointed out in Chapter 1, our main focus was
not on feature engineering. Dave, Lawrence, and Pennock (2003) also took the supervised learning approach and experimented with multiple datasets of product
reviews. They examined an extensive array of more sophisticated features based
on linguistic knowledge or heuristics, coupled with various feature-selection and
smoothing techniques. Depending on the specific experimental setting, results are
mixed compared to applying standard machine technique to simple unigram features. For instance, while trigrams and bigrams can improve performance under
certain settings, interesting features based on dependency parsers failed to improve performance on their test sets. Subsequently various other types of features
or feature selection schemes have been explored (Mullen and Collier, 2004; Gamon,
2004; Matsumoto, Takamura, and Okumura, 2005; Edoardo M. Airoldi, 2006).
One interesting problem arising from the domain-specific nature of this problem
in the context of supervised learning is how to adapt the classifier to a new domain. Dave, Lawrence, and Pennock (2003) touch on this problem in their “followon” task where they apply the classifier trained on the pre-assembled dataset to
product reviews found in search results that consist of different types of documents. But they did not investigate the effect of training-test mis-match in detail.
Aue and Gamon (2005b) explored different approaches to customize a sentiment
classification system to a new target domain in the absence of large amounts of
labeled data. The different types of data they considered range from lengthy movie
reviews to short, phrase-level user feedback to web surveys. Due to the significant
differences in these domains (different subject matter as well as different styles and
lengths of writing), simply applying the classifier learned on data from one domain
can barely outperform the baseline for another domain. In fact, with 100 or 200
labeled items in the target domain, an EM algorithm that utilizes in-domain unlabeled data and ignores out-of-domain data altogether outperforms the method
based exclusively on (both in- and out-of-domain) labeled data. Read (2005) did
not explicitly address this domain transfer problem, but did find standard ma-
chine learning techniques to be both domain-dependent (with domains ranging
from movie reviews to newswire articles), and temporally-dependent (based on
datasets spanning different ranges of time periods but written at least one year
apart). He also explored an alternative source of labeled data: emoticons used in
Usenet newsgroup postings. The idea was to extract texts around smile or frown
emoticons and map the emoticons into polarity labels.
In contrast to approaches based on supervised or semi-supervised learning,
Turney’s (2002) work on classification of reviews applied a specific unsupervised
learning technique based on the mutual information between document phrases
and the words “excellent” and “poor”, where the mutual information is computed
using statistics on data gathered by a search engine. He worked with reviews on
different subjects, reporting accuracies ranging over 64% (movie reviews) to 84%
(automobile reviews). Beineke et al. (2004) experimented with an extension of
Turney’s method that also utilized a small set of labeled data. Most unsupervised
approaches to document-level polarity classification involve automatic labeling of
words or phrases by their sentiment polarity (or “semantic orientation”, as it is
commonly referred to in the literature). In fact, some studies are entirely about
building such lexicons. Hatzivassiloglou and McKeown (1997) presented an early
approach based on linguistic heuristics. The main idea there is to use information
extracted from conjunctions between adjectives in a large corpus — e.g., if we see
two adjectives being linked by but, this suggests that they are of opposite orientations; conversely, being linked by and could be an evidence for the adjectives
having the same orientation. The task is then casted into a clustering problem
with constraints based on these heuristics. Most later studies in this direction
start with a small set of seed words (in some cases, just two words with opposite
polarity), and use these seed words to (sometimes incrementally) tag other words.
Corpus-based approaches examine co-occurrences with seed words based on large
collections of text (Turney, 2002; Turney and Littman, 2003; Yu and Hatzivassiloglou, 2003; Aue and Gamon, 2005a), or search for the context-dependent labels by
taking into account local constraints (Popescu and Etzioni, 2005). Alternatively,
people have looked into exploiting knowledge encoded in WordNet such as relations
(synonymy, antonymy and hyponymy) and glosses (Kim and Hovy, 2004; Hu and
Liu, 2004; Kamps et al., 2004; Takamura, Inui, and Okumura, 2005; Andreevskaia
and Bergler, 2006)
Subjectivity detection Work in polarity classification often assume the incoming documents to be opinionated. For many applications, we may need to
decide whether a given document contains subjective information or not, or identify which portions of the document are subjective. An early work in this direction
by Hatzivassiloglou and Wiebe (2000) examined the effects of adjective orientation
and gradability on sentence subjectivity. The goal was to tell whether a given sentence is subjective or not judging from the adjectives appearing in that sentence.
A number of projects address sentence-level or sub-sentence-level subjectivity detection in different domains (Wiebe, Wilson, and Bell, 2001; Wiebe and Wilson,
2002; Yu and Hatzivassiloglou, 2003; Riloff and Wiebe, 2003; Pang and Lee, 2004;
Beineke et al., 2004; Kim and Hovy, 2005a; Wilson, Wiebe, and Hoffmann, 2005).
Wiebe et al. (2004) present a comprehensive examination of using different clues
and features for recognizing subjectivity in text. Subjectivity detection at the document level is closely related to some studies in genre classification (see Section 2.2
for more detail). For instance, Yu and Hatzivassiloglou (2003) achieve high accu-
racy (97%) with a Naive Bayes classifier on a particular corpus consisting of Wall
Street Journal articles, where the task is to distinguish articles under News and
Business (facts) from articles under Editorial and Letter to the Editor (opinions).
Work in this direction is not limited to the binary distinction between subjective
and objective labels. Wilson, Wiebe, and Hwa (2004) addressed the problem of
determining clause-level opinion strength (e.g., “how mad are you”). Recent work
also considered the relations between word sense disambiguation and subjectivity
(Wiebe and Mihalcea, 2006).
Opinion mining and summarization An opinion-bearing document may contain off-topic passages that the readers may not be interested in. Hence, one possible approach to extract sentiment polarity on a given topic only is to first apply
a sentence-level classifier to identify topical sentences (Hurst and Nigam, 2004).
For reviews that cover several aspects of the subject matter, it may be desirable to
summarize the opinions grouped by the specific aspects addressed, and there has
been work that explored identifying features and opinions associated with these
features from product reviews (Yi et al., 2003; Hu and Liu, 2004; Kobayashi et al.,
2004; Popescu and Etzioni, 2005; Yi and Niblack, 2005).
Perspectives and viewpoints Some early work on non-factual-based text analysis dealt with perspectives and viewpoints (Wiebe and Rapaport, 1988; Wiebe,
1994). The MPQA (Multi Perspective Question Answering) project focuses on
question-answering tasks that require the ability to analyze opinions in text, so
that answers to questions such as “Was the most recent presidential election in
Zimbabwe regarded as a fair election?” can be extracted from collections of documents. Low-level opinion annotations were developed and evaluated, which facil-
itated the study of a number of interesting problems such as identifying opinion
holder and analyzing opinions at phrase level (Cardie et al., 2003; Stoyanov et al.,
2004; Breck and Cardie, 2004; Wilson, Wiebe, and Hwa, 2004; Choi et al., 2005;
Wiebe, Wilson, and Cardie, 2005; Kim and Hovy, 2005b). There has also been
work that focused on specific pairs of perspectives such as identifying Israeli versus
Palestinian viewpoints (Lin et al., 2006).
Affects and emotions People have considered various affect types, or the six
“universal” emotions (Ekman, 1982): anger, disgust, fear, happiness, sadness, and
surprise (Subasic and Huettner, 2001; Liu, Lieberman, and Selker, 2003; Alm,
Roth, and Sproat, 2005), as well as computational approaches for humor recognition and generation (Mihalcea and Strapparava, 2006). Many interesting aspects of
text like “happiness” or “mood” are also being explored in the context of informal
text resources such as weblogs (Nicolov et al., 2006).
Other non-factual information in text
Another related area of research that addresses non-topic-based categorization
is that of determining the genre of texts (Karlgren and Cutting, 1994; Kessler,
Nunberg, and Schütze, 1997; Stamatatos, Fakotakis, and Kokkinakis, 2000; Finn,
Kushmerick, and Smyth, 2002; Lee and Myaeng, 2002; Finn and Kushmerick,
2006). Since subjective genres, such as “editorial”, are often one of the possible
categories, such work can be closely related to subjectivity detection. There has
also been research that concentrates on classifying documents according to their
source or source style, with statistically-detected stylistic variation (Biber, 1988)
serving as an important cue. Authorship identification is perhaps the most salient
example — Mosteller and Wallace’s (1984) classic Bayesian study of the authorship of the Federalist Papers is one well-known instance. Argamon-Engelson et al.
(1998b) consider the related problem of identifying not the particular author of a
text, but its publisher (e.g. the New York Times vs. The Daily News); the work of
Kessler, Nunberg, and Schütze (1997) on determining a document’s “brow” (e.g.,
high-brow vs. “popular”, or low-brow) has similar goals. Several recent workshops
are dedicated to style analysis in text (Argamon, 2003; Argamon, Karlgren, and
Shanahan, 2005; Argamon, Karlgren, and Uzuner, 2006). Finally, Tomokiyo and
Jones (2001) studied an additional variant of the problem: distinguishing native
from non-native English speaker transcripts.
Chapter 3
Polarity classification1
As mentioned above, today, very large amounts of information are available in online documents, and a growing portion of such information comes in the form of
people’s experiences and opinions. It would be helpful for companies, recommender
systems, and review or editorial sites to automatically compile digests of such
information. It has proven quite useful in such contexts to create summaries of
people’s experiences and opinions that consist of subjective expressions extracted
from reviews (as is commonly done in movie ads) or even just a review’s polarity
— positive (“thumbs up”) or negative (“thumbs down”).
This chapter describes our work on polarity classification, where we label an
opinionated document as either positive or negative according to the author’s overall opinion towards the subject matter expressed through the text. As we briefly
discussed in Chapter 1, a challenging aspect of this problem that seems to dis1
This chapter is based on the work described in “Thumbs up? Sentiment Classification using Machine
Learning Techniques” with Lillian Lee and Shivakumar Vaithyanathan, which appeared in the proceedings of EMNLP 2002 (Pang, Lee, and Vaithyanathan, 2002) and “A Sentimental Education: Sentiment
Analysis Using Subjectivity Summarization Based on Minimum Cuts” with Lillian Lee, which appeared
in the proceedings of ACL 2004 (Pang and Lee, 2004).
tinguish it from traditional topic-based classification is that while topics are often
identifiable by keywords alone, sentiment can be expressed more implicitly. For
example, the sentence “how could anyone sit through that movie?” contains no
single word that is clearly negative (see Section 3.2.4 for more examples). Thus,
sentiment seems to require more understanding than the usual topic-based classification.
We first present our results obtained via machine learning techniques, treating
this problem as a standard text classification problem; we also analyze the problem
to gain a better understanding of how difficult it is. We then propose an approach
where we first remove objective sentences from the input document via an automatic subjectivity classifier and apply a standard machine-learning classifier to the
resulting subjective extract. This allows the classifier to focus on the portions of
the document that actually express sentiment; at the same time, this provides a
summary of the sentiment-oriented content of the document that can be potentially useful to the users as well. In particular, we experiment with an algorithm
based on graph formulation that incorporates intra-document relationships.
Movie Review Data
The work described in this chapter is based on two corpora we developed (Pang,
Lee, and Vaithyanathan, 2002; Pang and Lee, 2004). We have made these corpora
(and other related datasets described in this thesis) publicly available2 , and these
datasets have been adopted by more than a dozen other projects3 . These corpora
are in the movie review domain, which is experimentally convenient because there
are large on-line collections of such reviews, often accompanied by information
that provides machine-extractable labels. It is important to point out that our
methods are in no sense specific to this domain, and should be easily applicable to
other domains as long as sufficient training data exists. In addition, movie reviews
were found to be more difficult to classify than other product reviews (Turney,
(2002); but cf. Aue and Gamon (2005b), where movie reviews would found to be
easier than book reviews and user feedback). We next describe these corpora in
more detail. For other datasets suitable for work in sentiment analysis that we
have collected and distributed, see Section 4.4 for a description on a scale dataset
with a collection of documents whose labels come from a rating scale, and Section
5.2 for more details on a political speech dataset containing a set of congressional
speeches with polarity labels (support or oppose).
Polarity Dataset The polarity dataset is a set of movie reviews with polarity labels which we created for our work on polarity classification. In many movie-review
sites, reviewers summarize their overall sentiment with a machine-extractable rating indicator, such as a number of stars; hence, we did not need to hand-label the
data for supervised learning or evaluation purposes.
Our data source was the Internet Movie Database (IMDb) archive of the newsgroup.4 We selected only reviews where the author rating was expressed either with stars or some numerical value (other conventions varied too widely to allow for automatic processing). Ratings were automatically extracted and converted into one of three categories: positive, negative, or
neutral. As the work described in this chapter was among the early attempts at
automatically classifying documents based on sentiment, we did not have a firm
grip of the difficulty of the task, and thus concentrated only on discriminating be4
tween positive and negative sentiment as a reasonable first step. In fact, given the
subtle nature of how sentiment is expressed in text, even two-class classification
problem may appear quite daunting
See Chapter 4 for our experiments with all three categories as well as even more
fine-grained classification.
To avoid domination of the corpus by a small number of prolific reviewers, we
imposed a limit of fewer than 20 reviews per author per sentiment category, yielding
a corpus of 752 negative and 1301 positive reviews, with a total of 144 reviewers
represented. Version 1.0 of the polarity dataset (Pang, Lee, and Vaithyanathan,
2002) consists of 700 negative and 700 positive reviews randomly selected from that
corpus. We later expanded this collection through an enhanced rating extraction
method that covers a broader range of rating expressions and uses a more reliable
conversion scheme. This resulted in Version 2.0 of the polarity dataset, which
consists of 1000 positive and 1000 negative reviews (all written before 2002), with
a total of 312 authors represented (Pang and Lee, 2004).
Subjectivity Dataset This dataset consists of sentences together with subjectivity labels (“subjective” vs “objective”). Riloff and Wiebe (2003) state that “It
is [very hard] to obtain collections of individual sentences that can be easily identified as subjective or objective”; the polarity-dataset sentences, for example, have
not been so annotated. Fortunately, we were able to mine the Web to create a
large, automatically-labeled sentence corpus. To gather subjective sentences (or
phrases), we collected 5000 movie-review snippets (e.g., “bold, imaginative, and
impossible to resist”) from To obtain (mostly) objective data, we took 5000 sentences from plot summaries available from the Internet
Movie Database ( 5 . We only selected sentences or snippets at
least ten words long and drawn from reviews or plot summaries of movies released
post-2001, which prevents overlap with the polarity dataset.
Polarity Classification via Machine Learning Methods
This section presents our work on polarity classification with standard machine
learning methods using version 1.0 of the polarity dataset.
A Closer Look At the Problem
At the time this work begun, intuitions seemed to differ as to the difficulty of
the sentiment detection problem. An expert on using machine learning for text
categorization predicted relatively low performance for automatic methods. On the
other hand, it seems that distinguishing positive from negative reviews is relatively
easy for humans, especially in comparison to the standard text categorization
problem, where topics can be closely related. One might also suspect that there
are certain words people tend to use to express strong opinions, so that it might
suffice to simply produce a list of such words by introspection and rely on them
alone to classify the texts. Indeed, this promise seems to underly early work on
using manually-treated lexicons of indicative terms (Huettner and Subasic, 2000;
Das and Chen, 2001).
To test this latter hypothesis, we asked two graduate students in computer
science to (independently) choose good indicator words for positive and negative
sentiments in movie reviews. Their selections, shown in Figure 3.1, seem intuitively
These sentences (snippets) are randomly selected from the 5856 objective sentences (from plot summaries for movies released in 2002 and 2003) and 12763 snippets (from reviews for 2002 movies) we
collected, where a total of 812 authors from 303 sources were represented.
Human 1
Proposed word lists
positive: dazzling, brilliant, phenomenal, excellent, fantastic
negative: suck, terrible, awful, unwatchable, hideous
Human 2
positive: gripping, mesmerizing, riveting, spectacular, cool,
awesome, thrilling, badass, excellent, moving, exciting
negative: bad, cliched, sucks, boring, stupid, slow
Figure 3.1: Baseline results for human word lists. Data: 700 positive and 700
negative reviews.
Proposed word lists
Human 3
positive: love, wonderful, best, great, superb, still, beautiful
negative: bad, worst, stupid, waste, boring, ?, !
Figure 3.2: Corresponding baselines using introspection and simple statistics of
the test data.
plausible. We then converted their responses into simple decision procedures that
essentially count the number of the proposed positive and negative words in a given
document. We applied these procedures to a dataset in which the random-choice
baseline result would be 50%.
As shown in Figure 3.1, the accuracy — percentage of documents classified
correctly — for the human-based classifiers were 58% and 64%, respectively.6 Note
that the tie rates — percentage of documents where the two sentiments were rated
equally likely — are quite high7 (we chose a tie breaking policy that maximized
the accuracy of these baselines).
Later experiments using these words as features for machine learning methods did not yield better
This is largely due to 0-0 ties.
While the tie rates suggest that the brevity of the human-produced lists is a
factor in the relatively poor performance results, it is not the case that size alone
necessarily limits accuracy. Based on a very preliminary examination of frequency
counts in the entire corpus (including test data) plus introspection, we created
a list of seven positive and seven negative words (including punctuation), shown
in Figure 3.2. As that figure indicates, using these words raised the accuracy to
69%. Also, although this third list is of comparable length to the other two, it has
a much lower tie rate of 16%. We further observe that some of the items in this
third list, such as “?” or “still”, would probably not have been proposed as possible
candidates merely through introspection, although upon reflection one sees their
merit (the question mark tends to occur in sentences like “What was the director
thinking?”; “still” appears in sentences like “Still, though, it was worth seeing”).
We conclude from these preliminary experiments that it is worthwhile to explore corpus-based techniques, rather than relying on prior intuitions, to select
good indicator features and to perform sentiment classification in general. These
experiments also provide us with baselines for experimental comparison; in particular, the third baseline of 69% might actually be considered somewhat difficult to
beat, since it was achieved by examination of the test data (although our examination was rather cursory; we do not claim that there is no better-performing set
of fourteen words).
Machine Learning Methods
Our aim in this work was to examine whether it suffices to treat sentiment classification simply as a special case of text categorization (with the two “topics”
being positive sentiment and negative sentiment), or whether special sentiment-
categorization methods need to be developed. We experimented with three standard algorithms: Naive Bayes classification, maximum entropy classification, and
support vector machines. The philosophies behind these three algorithms are quite
different, but each has been shown to be effective in previous text categorization
To implement these machine learning algorithms on our document data, we
used the following standard bag-of-features framework. Let {f1 , . . . , fm } be a predefined set of m features that can appear in a document; examples include the
word “still” or the bigram “really stinks”. Let ni (d) be the number of times fi
occurs in document d. Then, each document d is represented by the document
vector d~ := (n1 (d), n2 (d), . . . , nm (d)).
Naive Bayes
One approach to text classification is to assign to a given document d the class
C ∗ = arg maxC P (C | d). We derive the Naive Bayes (NB) classifier by first
observing that by Bayes’ rule,
P (C | d) =
P (C)P (d | C)
P (d)
where P (d) plays no role in selecting C ∗ . To estimate the term P (d | C), Naive
Bayes decomposes it by assuming the fi ’s are conditionally independent given d’s
PNB (C | d) :=
P (C)
P (fi | C)ni (d)
P (d)
Our training method consists of relative-frequency estimation of P (C) and P (fi | C),
using add-one smoothing.
Despite its simplicity and the fact that its conditional independence assumption
clearly does not hold in real-world situations, Naive Bayes-based text categoriza-
tion still tends to perform surprisingly well (Lewis, 1998); indeed, Domingos and
Pazzani (1997) show that Naive Bayes is optimal for certain problem classes with
highly dependent features. On the other hand, more sophisticated algorithms
might (and often do) yield better results; we examine two such algorithms next.
Maximum Entropy
Maximum entropy classification (MaxEnt, or ME, for short) is an alternative technique which has proven effective in a number of natural language processing applications (Berger, Della Pietra, and Della Pietra, 1996). Nigam, Lafferty, and McCallum (1999)
show that it sometimes, but not always, outperforms Naive Bayes at standard text
classification. Its estimate of P (C | d) takes the following exponential form:
PME (C | d) :=
λi,C Fi,C (d, C) ,
where Z(d) is a normalization function. Fi,C is a feature/class function for feature
fi and class C, defined as follows:8
1, ni (d) > 0 and C 0 = C
Fi,C (d, C ) :=
0 otherwise
For instance, a particular feature/class function might fire if and only if the bigram “still hate” appears and the document’s sentiment is hypothesized to be
negative.9 Importantly, unlike Naive Bayes, MaxEnt makes no assumptions about
the relationships between features, and so might potentially perform better when
conditional independence assumptions are not met.
We use a restricted definition of feature/class functions so that MaxEnt relies on the same sort of
feature information as Naive Bayes.
Nigam, Lafferty, and McCallum (1999) for additional motivation.
The λi,C ’s are feature-weight parameters; inspection of the definition of PME
shows that a large λi,C means that fi is considered a strong indicator for class C.
The parameter values are set so as to maximize the entropy of the induced distribution (hence the classifier’s name) subject to the constraint that the expected
values of the feature/class functions with respect to the model are equal to their
expected values with respect to the training data: the underlying philosophy is
that we should choose the model making the fewest assumptions about the data
while still remaining consistent with it, which makes intuitive sense. We use ten
iterations of the improved iterative scaling algorithm (Della Pietra, Della Pietra,
and Lafferty, 1997) for parameter training (this was a sufficient number of iterations for convergence of training-data accuracy), together with a Gaussian prior
to prevent overfitting (Chen and Rosenfeld, 2000).
Support Vector Machines
Support vector machines (SVMs) have been shown to be highly effective at traditional text categorization, generally outperforming Naive Bayes (Joachims, 1998).
They are large-margin, rather than probabilistic, classifiers, in contrast to Naive
Bayes and MaxEnt. In the two-category case, the basic idea behind the training
procedure is to find a hyperplane, represented by vector w,
~ that not only separates the document vectors in one class from those in the other, but for which
the separation, or margin, is as large as possible. This search corresponds to a
constrained optimization problem; letting Cj ∈ {1, −1} (corresponding to positive
and negative) be the correct class of document dj , the solution can be written as
αj Cj d~j , αj ≥ 0;
the αj ’s are obtained by solving a dual optimization problem. Those d~j such
that αj is greater than zero are called support vectors, since they are the only
document vectors contributing to w.
~ Classification of test instances consists simply
of determining which side of w’s
~ hyperplane they fall on.
We used Joachim’s (1999) SV M light package10 for training and testing, with
all parameters set to their default values, after first length-normalizing the document vectors, as is standard (neglecting to normalize generally hurt performance
Experimental Set-up
We used documents from the polarity dataset (version 1.0) described in Section
3.1.1. We divided this data into three equal-sized folds, maintaining balanced class
distributions in each fold. (We did not use a larger number of folds due to the
slowness of the MaxEnt training procedure.) All results reported below, as well as
the baseline results from Section 3.2.1, are the average three-fold cross-validation
results on this data (of course, the baseline algorithms had no parameters to tune).
To prepare the documents, we automatically removed the rating indicators
and extracted the textual information from the original HTML document format,
treating punctuation marks as separate lexical items. No stemming or stoplists
were used.
One unconventional step we took was to attempt to model the potentially important contextual effect of negation: clearly “good” and “not very good” indicate
opposite sentiment orientations. Adapting a technique of Das and Chen (2001),
we added the tag NOT to every word between a negation word (“not”, “isn’t”,
“didn’t”, etc.) and the first punctuation mark following the negation word. (Preliminary experiments indicate that removing the negation tag had a negligible, but
on average slightly harmful, effect on performance.)
For this study, we focused on features based on unigrams (with negation tagging) and bigrams. Because training MaxEnt is expensive in the number of features, we limited consideration to (1) the 16165 unigrams appearing at least four
times in our 1400-document corpus (lower count cutoffs did not yield significantly
different results), and (2) the 16165 bigrams occurring most often in the same data
(the selected bigrams all occurred at least seven times). Note that we did not add
negation tags to the bigrams, since we consider bigrams (and n-grams in general)
to be an orthogonal way to incorporate context.
Initial unigram results The classification accuracies resulting from using only
unigrams as features are shown in line (1) of Table 3.1. As a whole, the machine
learning algorithms clearly surpass the random-choice baseline of 50%. They also
handily beat our two human-selected-unigram baselines of 58% and 64%, and,
furthermore, perform well in comparison to the 69% baseline achieved via limited
access to the test-data statistics, although the improvement in the case of SVMs
is not so large.
On the other hand, in topic-based classification, all three classifiers have been
reported to use bag-of-unigram features to achieve accuracies of 90% and above
for particular categories (Joachims, 1998; Nigam, Lafferty, and McCallum, 1999)11
Joachims (1998) used stemming and stoplists;
Nigam, Lafferty, and McCallum (1999), like us, did not.
Table 3.1: Average three-fold cross-validation accuracies, in percent. Boldface:
best performance for a given setting (row). Recall that our baseline results ranged
from 50% to 69%.
# of
frequency or
top 2633 unigrams
(8) unigrams+position
— and such results are for settings with more than two classes. This provides
suggestive evidence that sentiment categorization is more difficult than topic classification, which corresponds to the intuitions of the text categorization expert
mentioned above.12 Nonetheless, we still wanted to investigate ways to improve
our sentiment categorization results; these experiments are reported below.
Feature frequency vs. presence Recall that we represent each document d by
a feature-count vector (n1 (d), . . . , nm (d)). However, the definition of the MaxEnt
feature/class functions Fi,c only reflects the presence or absence of a feature, rather
We could not perform the natural experiment of attempting topic-based categorization on our data
because the only obvious topics would be the film being reviewed; unfortunately, in our data, the maximum number of reviews per movie is 27, too small for meaningful results.
than directly incorporating feature frequency. In order to investigate whether
reliance on frequency information could account for the higher accuracies of Naive
Bayes and SVMs, we binarized the document vectors, setting ni (d) to 1 if and
only feature fi appears in d, and reran Naive Bayes and SV M light on these new
As can be seen from line (2) of Table 3.1, better performance (much better
performance for SVMs) is achieved by accounting only for feature presence, not
feature frequency. Interestingly, this is in direct opposition to the observations of
McCallum and Nigam (1998) with respect to Naive Bayes topic classification. We
speculate that this indicates a difference between sentiment and topic categorization — perhaps due to topic being conveyed mostly by particular content words
that tend to be repeated — but this remains to be verified. In any event, as a result
of this finding, we did not incorporate frequency information into Naive Bayes and
SVMs in any of the following experiments.
Bigrams In addition to looking specifically for negation words in the context
of a word, we also studied the use of bigrams to capture more context in general.
Note that bigrams and unigrams are surely not conditionally independent, meaning
that the feature set they comprise violates Naive Bayes’ conditional-independence
assumptions; on the other hand, recall that this does not imply that Naive Bayes
will necessarily do poorly (Domingos and Pazzani, 1997).
Line (3) of the results table shows that bigram information does not improve
performance beyond that of unigram presence, although adding in the bigrams does
Alternatively, we could have tried integrating frequency information into MaxEnt. However, feature/class functions are traditionally defined as binary (Berger, Della Pietra, and Della Pietra, 1996);
hence, explicitly incorporating frequencies would require different functions for each count (or count bin),
making training impractical. But cf. Nigam, Lafferty, and McCallum (1999).
not seriously impact the results, even for Naive Bayes. This would not rule out the
possibility that bigram presence is as equally useful a feature as unigram presence;
in fact, Pedersen (2001) found that bigrams alone can be effective features for word
sense disambiguation. However, comparing line (4) to line (2) shows that relying
just on bigrams causes accuracy to decline by as much as 5.8 percentage points.
Hence, if context is in fact important, as our intuitions suggest, bigrams are not
effective at capturing it in our setting.
Parts of speech We also experimented with appending POS tags to every word
via Oliver Mason’s Qtag program.14 This serves as a crude form of word sense
disambiguation (Wilks and Stevenson, 1998): for example, it would distinguish the
different usages of “love” in “I love this movie” (indicating sentiment orientation)
versus “This is a love story” (neutral with respect to sentiment). However, the
effect of this information seems to be a wash: as depicted in line (5) of Table 3.1,
the accuracy improves slightly for Naive Bayes but declines for SVMs, and the
performance of MaxEnt is unchanged.
Since adjectives have been a focus of previous work in sentiment detection
(Hatzivassiloglou and Wiebe, 2000; Turney, 2002), we looked at the performance
of using adjectives alone.15 Intuitively, we might expect that adjectives carry a
great deal of information regarding a document’s sentiment; indeed, the humanproduced lists from Section 3.2.1 contain almost no other parts of speech. Yet,
the results, shown in line (6) of Table 3.1, are relatively poor: the 2633 adjectives
provide less useful information than unigram presence. Indeed, line (7) shows
that simply using the 2633 most frequent unigrams is a better choice, yielding
Turney’s (2002) unsupervised algorithm uses bigrams containing an adjective or an adverb.
performance comparable to that of using (the presence of) all 16165 (line (2)).
This may imply that applying explicit feature-selection algorithms on unigrams
could improve performance.
Position An additional intuition we had was that the position of a word in
the text might make a difference: movie reviews, in particular, might begin with
an overall sentiment statement, proceed with a plot discussion, and conclude by
summarizing the author’s views. As a rough approximation to determining this
kind of structure, we tagged each word according to whether it appeared in the
first quarter, last quarter, or middle half of the document16 . The results (line
(8)) didn’t differ greatly from using unigrams alone, but more refined notions of
position might be more successful.
The results produced via machine learning techniques are quite good in comparison
to the human-generated baselines discussed in Section 3.2.1. In terms of relative
performance, Naive Bayes tends to do the worst and SVMs tend to do the best,
although the differences aren’t very large.
On the other hand, we were not able to achieve accuracies on the sentiment
classification problem comparable to those reported for standard topic-based categorization, despite the several different types of features we tried. Unigram presence information turned out to be the most effective; in fact, none of the alternative features we employed provided consistently better performance once unigram
presence was incorporated. Interestingly, though, the superiority of presence infor16
We tried a few other settings, e.g. first third vs. last third vs middle third, and found them to be
less effective.
mation in comparison to frequency information in our setting contradicts previous
observations made in topic-classification work (McCallum and Nigam, 1998).
What accounts for these two differences — difficulty and types of information
proving useful — between topic and sentiment classification, and how might we
improve the latter? To answer these questions, we examined the data further. (All
examples below are drawn from the full 2053-document corpus.)
Consider the word “good”, which would at first appear to be a strong indicator
for positive sentiment. However, it appears 478 times in positive reviews and 411
in negative reviews. The reason “good” occurs frequently in the negative class
is not just because people use it in sentiment-neutral phrases. In many negative
reviews, it is still being used in its subjective sense, as in the following “thwarted
expectations” narrative, where the author sets up a deliberate contrast to earlier
discussion: for example,
This film should be brilliant. It sounds like a great plot, the actors
are first grade, and the supporting cast is good as well, and Stallone is
attempting to deliver a good performance. However, it can’t hold up.
We also have many instances of the converse occurrence:
Some horror films just get off on non-stop gore, cheap thrills, and, well,
bad acting. “Alien” has none of these.
In the latter example, the issue appears to be one of reference resolution: it would
certainly help if we could recognize that “bad acting” is not referring to the subject
matter of this review. Co-reference resolution is a hard enough problem in itself,
but we also have example like this: or
I hate the Spice Girls. ...[3 things the author hates about them]... Why
I saw this movie is a really, really, really long story, but I did, and
one would think I’d despise every minute of it. But... Okay, I’m really
ashamed of it, but I enjoyed it. I mean, I admit it’s a really awful movie,
...the ninth floor of hell...The plot is such a mess that it’s terrible. But
I loved it.
Note that this phenomenon is related to another common theme, that of “a good
actor trapped in a bad movie”:
AN AMERICAN WEREWOLF IN PARIS is a failed attempt... Julie
Delpy is far too good for this movie. She imbues Serafine with spirit,
spunk, and humanity. This isn’t necessarily a good thing, since it prevents us from relaxing and enjoying AN AMERICAN WEREWOLF
IN PARIS as a completely mindless, campy entertainment experience.
Delpy’s injection of class into an otherwise classless production raises
the specter of what this film could have been with a better script and a
better cast ... She was radiant, charismatic, and effective ....
This example is particularly striking in that a human wouldn’t need to read past
the first sentence, but unfortunately, the techniques we used in our experiments
will, and will potentially be fooled by the many accolades for the actor rather than
the film itself.
In these examples, a human would easily detect the true sentiment of the review,
but bag-of-features classifiers would presumably find these instances difficult, since
there are many words indicative of the opposite sentiment to that of the entire
review. Fundamentally, it seems that some form of discourse analysis is necessary
(using more sophisticated techniques than our positional feature mentioned above),
or at least some way of determining the focus of each sentence, so that one can
decide when the author is talking about the film itself. (Turney (2002) makes a
similar point, noting that for reviews, “the whole is not necessarily the sum of the
parts”.) Furthermore, it seems likely that this thwarted-expectations rhetorical
device will appear in many types of texts (e.g., editorials) devoted to expressing
an overall opinion about some topic. Hence, we believe that an important next step
is the identification of features indicating whether sentences are on-topic (which
is a kind of co-reference problem); we look forward to addressing this challenge in
future work.
Polarity classification with subjective summaries
In contrast to previous approaches that focused on selecting indicative lexical features (e.g., the word “good”), classifying a document according to the number
of such features that occur anywhere within it, we proposed a two-step process
that uses subjective summarizations to help polarity classification (1) label the
sentences in the document as either subjective or objective, discarding the latter;
and then (2) apply a standard machine-learning classifier to the resulting extract.
This can prevent the polarity classifier from considering irrelevant or even potentially misleading text: for example, although the sentence “The protagonist tries
to protect her good name” contains the word “good”, it could well be embedded
in a negative movie review. Also, as mentioned above, the extract itself can be
provided to users as a summary of the subjective content of the document. In
particular, we experimented with using a framework based on finding minimum
cuts to model context information.
One can consider document-level polarity classification to be just a special (more
difficult) case of text categorization with sentiment- rather than topic-based categories. Hence, standard machine-learning classification techniques, such as support
vector machines, can be applied to the entire documents themselves, as was done in
Section 3.2. We refer to such classification techniques as default polarity classifiers.
However, as noted above, we may be able to improve polarity classification
by removing objective sentences (such as plot summaries in a movie review). We
therefore propose, as depicted in Figure 3.3, to first employ a subjectivity detector
that determines whether each sentence is subjective or not: discarding the objective
ones creates an extract that should better represent a review’s subjective content
to a default polarity classifier.
m−sentence extract
positive or negative
n−sentence review
subjectivity extraction
Figure 3.3: Polarity classification via subjectivity detection.
To our knowledge, previous work has not integrated sentence-level subjectivity
detection with document-level sentiment polarity. Yu and Hatzivassiloglou (2003)
provide methods for sentence-level analysis and for determining whether a docu-
ment is subjective or not, but do not combine these two types of algorithms or consider document polarity classification. The motivation behind the single-sentence
selection method of Beineke et al. (2004) is to reveal a document’s sentiment polarity, but they do not evaluate the polarity-classification accuracy that results.
Context and Subjectivity Detection
As with document-level polarity classification, we could perform subjectivity detection on individual sentences by applying a standard classification algorithm on
each sentence in isolation. However, modeling proximity relationships between
sentences would enable us to leverage coherence: text spans occurring near each
other (within discourse boundaries) may share the same subjectivity status, other
things being equal (Wiebe, 1994).
We would therefore like to supply our algorithms with pair-wise interaction information, e.g., to specify that two particular sentences should ideally receive the
same subjectivity label — without stating which label this should be. This is difficult to do with classifiers whose input consists simply of individual feature vectors,
such as naive Bayes. For example, one could try adding extra dummy features for
which the two sentences have the same value, but what values for these features
should all the other sentences have? Fortunately, we can overcome this problem
using a graph-based formulation relying on finding minimum cuts. This was first
pointed in the machine-learning literature by Blum and Chawla (2001), although
they focused on similarity between items (the motivation being to combine labeled
and unlabeled data), whereas we are concerned with physical proximity between
the items to be classified.
ind 1(M) [.5]
ind 2(Y) [.2]
ind 1(Y) [.8]
ind 2(M) [.5]
ind 1(N) [.1]
ind 2(N) [.9]
.2 + .5 + .1
.1 + .2
.8 + .5 + .1
.2 + .5 + .9
.2 + .5 + .1
1.0 + .1
.8 + .5 + .9
.1 + .2
.8 + .5 + .1
1.0 + .2
.2 + .5 + .9
1.0 + .2
.8 + .5 + .9
1.0 + .1
Figure 3.4: Graph for classifying three items. Brackets enclose example values;
here, the individual scores happen to be probabilities. Based on individual scores
alone, we would put Y (“yes”) in C1 , N (“no”) in C2 , and be undecided about M
(“maybe”). But the association scores favor cuts that put Y and M in the same
class, as shown in the table. Thus, the minimum cut, indicated by the dashed line,
places M together with Y in C1 .
Cut-based classification
Figure 3.4 shows a worked example of the concepts in this section.
Suppose we have n items x1 , . . . , xn to divide into two classes C1 and C2 , and
we have access to two types of information:
• Individual scores indj (xi ): non-negative estimates of each xi ’s preference for
being in Cj based on just the features of xi alone; and
• Association scores assoc(xi , xk ): non-negative estimates of how important it
is that xi and xk be in the same class.17
Asymmetry is allowed, but we used symmetric scores.
We would like to maximize each item’s “net happiness”: its individual score for
the class it is assigned to, minus its individual score for the other class. But, we
also want to penalize putting tightly-associated items into different classes. Thus,
after some algebra, we arrive at the following optimization problem: assign the xi s
to C1 and C2 so as to minimize the partition cost
ind2 (x) +
ind1 (x) +
assoc(xi , xk ).
xi ∈C1 ,
xk ∈C2
The problem appears intractable, since there are 2n possible binary partitions
of the xi ’s. However, suppose we represent the situation in the following manner.
Build an undirected graph G with vertices {v1 , . . . , vn , s, t}; the last two are, respectively, the source and sink. Add n edges (s, vi ), each with weight ind1 (xi ), and
n edges (vi , t), each with weight ind2 (xi ). Finally, add n2 edges (vi , vk ), each with
weight assoc(xi , xk ). Then, cuts in G are defined as follows:
Definition 1 A cut (S, T ) of G is a partition of its nodes into sets S = {s} ∪ S 0
and T = {t} ∪ T 0 , where s 6∈ S 0 , t 6∈ T 0 . Its cost cost(S, T ) is the sum of the weights
of all edges crossing from S to T . A minimum cut of G is one of minimum cost.
Observe that every cut corresponds to a partition of the items and has cost equal
to the partition cost. Thus, our optimization problem reduces to finding minimum
Practical advantages As we have noted, formulating our subjectivity-detection
problem in terms of graphs allows us to model item-specific and pairwise information independently. Note that this is a very flexible paradigm. For instance, it
is perfectly legitimate to use knowledge-rich algorithms employing deep linguistic
knowledge about sentiment indicators to derive the individual scores. And we could
also simultaneously use knowledge-lean methods to assign the association scores.
Interestingly, Yu and Hatzivassiloglou (2003) compared an individual-preference
classifier against a relationship-based method, but didn’t combine the two; the
ability to coordinate such algorithms is precisely one of the strengths of our approach.
But a crucial advantage specific to the utilization of a minimum-cut-based approach is that we can use maximum-flow algorithms with polynomial asymptotic
running times — and near-linear running times in practice — to exactly compute the minimum-cost cut(s), despite the apparent intractability of the optimization problem (Cormen, Leiserson, and Rivest, 1990; Ahuja, Magnanti, and Orlin,
1993).18 In contrast, other graph-partitioning problems that have been previously
used to formulate NLP classification problems19 are NP-complete (Hatzivassiloglou
and McKeown, 1997; Agrawal et al., 2003; Joachims, 2003). In the next secion,
We examine in the next more detail related work using graph-based methods.
Related work for the graph-cut formulation
As we mentioned earlier, our approach is inspired by Blum and Chawla (2001),
who used minimum cuts as a means to integrate labeled and unlabeled data, reporting improvements over ID3 and 3-nearest-neighbors. Although they focused
on similarity between items (the motivation being to combine labeled and unlabeled data), whereas we are concerned with physical proximity between the items
to be classified; indeed, in computer vision, modeling proximity information via
graph cuts has led to very effective classification (Boykov, Veksler, and Zabih,
1999), which is another source of inspiration for our work. In addition to our
Code available at
Graph-based approaches to general clustering problems are too numerous to mention here.
own work described in this Chapter and Chapter 5, this framework has also been
exploited for other NLP tasks (Agarwal and Bhattacharyya, 2005; Barzilay and
Lapata, 2005; Malioutov and Barzilay, 2006). To our knowledge, the only prior
uses of maximum-flow techniques in NLP are the work of Gaussier (1998) and
Popescu, Etzioni, and Kautz (2003). But utilizing flow to compute matchings between items (e.g., French and English sentences in the first case), as these papers
do, does not make use of the crucial information-integration advantage conferred
by the minimum-cut formulation.
Work based on normalized cuts (Dhillon, 2001; Joachims, 2003) adds an extra
“balanced-size” constraint to the mix; but finding the optimal solution in this new
situation is NP-complete. This intractability also holds for maximum cut, used by
Agrawal et al. (2003) to find “camps” in newsgroups. In addition to the work of
Blum and Chawla (2001) and Joachims (2003), other notable early papers on
graph-based semi-supervised learning include Bansal, Blum, and Chawla (2002)
and Kondor and Lafferty (2002). Zhu (2005a) maintains a survey of this area.
Recently, several alternative, often quite sophisticated approaches to collective
classification have been proposed (Neville and Jensen, 2000; Lafferty, McCallum,
and Pereira, 2001; Getoor et al., 2002; Taskar, Abbeel, and Koller, 2002; Taskar,
Guestrin, and Koller, 2003; Taskar, Chatalbashev, and Koller, 2004; McCallum
and Wellner, 2004). It would be interesting to investigate the application of such
methods to our problem. However, we also believe that our approach has important
advantages, including conceptual simplicity and the fact that it is based on an
underlying optimization problem that is provably and in practice easy to solve.
m−sentence extract
n−sentence review
individual subjectivity−probability link
edge crossing the cut
proximity link
Figure 3.5: Graph-cut-based creation of subjective extracts.
Evaluation Framework
Data Our experiments involve classifying movie reviews as either positive or
negative; for this task, we used the polarity dataset (version 2.0) In addition,
to train our sentence-level subjectivity detectors, we used the subjectivity dataset
(see Section 3.1.1 for more detail).
Default polarity classifiers
We tested support vector machines (SVMs) and
Naive Bayes (NB). Following the work described in Section 3.2, we use unigrampresence features: the ith coordinate of a feature vector is 1 if the corresponding
unigram occurs in the input text, 0 otherwise. (For SVMs, the feature vectors are
length-normalized). Each default document-level polarity classifier is trained and
tested on the extracts formed by applying one of the sentence-level subjectivity
detectors to reviews in the polarity dataset.
Subjectivity detectors As noted above, we can use our default polarity classifiers as “basic” sentence-level subjectivity detectors (after retraining on the subjectivity dataset) to produce extracts of the original reviews. We also create a family
of cut-based subjectivity detectors; these take as input the set of sentences appear-
ing in a single document and determine the subjectivity status of all the sentences
simultaneously using per-item and pairwise relationship information. Specifically,
for a given document, we use the construction in Section 3.3.2 to build a graph
wherein the source s and sink t correspond to the class of subjective and objective
sentences, respectively, and each internal node vi corresponds to the document’s
(si ) and ind2 (si )
ith sentence si . We can set the individual scores ind1 (si ) to P rsub
to 1 − P rsub
(si ), as shown in Figure 3.5, where P rsub
(s) denotes Naive Bayes’
estimate of the probability that sentence s is subjective; or, we can use the weights
produced by the SVM classifier instead.20 If we set all the association scores to
zero, then the minimum-cut classification of the sentences is the same as that of the
basic subjectivity detector. Alternatively, we can incorporate the degree of proximity between pairs of sentences, controlled by three parameters. The threshold
T specifies the maximum distance two sentences can be separated by and still be
considered proximal. The non-increasing function f (d) specifies how the influence
of proximal sentences decays with respect to distance d; in our experiments, we
tried f (d) = 1, e1−d , and 1/d2 . The constant c controls the relative influence of the
association scores: a larger c makes the minimum-cut algorithm more loath to put
We converted any given SVM output di , which is a signed distance (negative=objective) from the
separating hyperplane, to a non-negative number by
ind1 (si ) = (2 + di )/4
di > 2;
−2 ≤ di ≤ 2;
di < −2.
and ind2 (si ) = 1 − ind1 (si ). Note that scaling is employed only for consistency; the algorithm itself does
not require probabilities for individual scores.
proximal sentences in different classes. With these in hand21 , we set (for j > i)
f (j − i) · c if (j − i) ≤ T ;
assoc(si , sj ) =
Experimental Results
Below, we report average accuracies computed by ten-fold cross-validation over
the polarity dataset. Section 3.3.6 examines our basic subjectivity extraction algorithms, which are based on individual-sentence predictions alone. Section 3.3.6
evaluates the more sophisticated form of subjectivity extraction that incorporates
context information via the minimum-cut paradigm.
As we will see, the use of subjectivity extracts can in the best case provide
satisfying improvement in polarity classification, and otherwise can at least yield
polarity-classification accuracies indistinguishable from employing the full review.
At the same time, the extracts we create are both smaller on average than the original document and more effective as input to a default polarity classifier than the
same-length counterparts produced by standard summarization tactics (e.g., firstor last-N sentences). We therefore conclude that subjectivity extraction produces
effective summaries of document sentiment.
Basic subjectivity extraction
As noted in Section 3.3.5, both Naive Bayes and SVMs can be trained on our
subjectivity dataset and then used as a basic subjectivity detector. The former has
somewhat better average ten-fold cross-validation performance on the subjectivity
Parameter training is driven by optimizing the performance of the downstream polarity classifier
rather than the detector itself because the subjectivity dataset’s sentences come from different reviews,
and so are never proximal.
dataset (92% vs. 90%), and so for clarity, our initial discussions will focus on the
results attained via NB subjectivity detection.
Employing Naive Bayes as a subjectivity detector (ExtractNB ) in conjunction
with a Naive Bayes document-level polarity classifier achieves 86.4% accuracy.22
This is a clear improvement over the 82.8% that results when no extraction is
applied (Full review); indeed, the difference is highly statistically significant (p <
0.01, paired t-test). With SVMs as the polarity classifier instead, the Full review
performance rises to 87.15%, but comparison via the paired t-test reveals that this
is statistically indistinguishable from the 86.4% that is achieved by running the
SVM polarity classifier on ExtractNB input. (More improvements to extraction
performance are reported later in this section.)
These findings indicate23 that the extracts preserve (and, in the NB polarityclassifier case, apparently clarify) the sentiment information in the originating
documents, and thus are good summaries from the polarity-classification point
of view.
Further support comes from a “flipping” experiment: if we give as
input to the default polarity classifier an extract consisting of the sentences labeled
objective, accuracy drops dramatically to 71% for NB and 67% for SVMs. This
confirms our hypothesis that sentences discarded by the subjectivity extraction
process are indeed much less indicative of sentiment polarity.
Moreover, the subjectivity extracts are much more compact than the original
documents (an important feature for a summary to have): they contain on average
only about 60% of the source reviews’ words. (This word preservation rate is
plotted along the x-axis in the graphs in Figure 3.7.) This prompts us to study
This result and others are depicted in Figure 3.7; for now, consider only the y-axis in those plots.
Recall that direct evidence is not available because the polarity dataset’s sentences lack subjectivity
Accuracy for N-sentence abstracts (def = NB)
Average accuracy
most subjective N sentences
last N sentences
first N sentences
least subjective N sentences
Full review
Accuracy for N-sentence abstracts (def = SVM)
Average accuracy
most subjective N sentences
last N sentences
first N sentences
least subjective N sentences
Full review
Figure 3.6: Accuracies using N-sentence extracts for NB (up) and SVM (down)
default polarity classifiers.
how much reduction of the original documents subjectivity detectors can perform
and still accurately represent the texts’ sentiment information.
We can create subjectivity extracts of varying lengths by taking just the N most
subjective sentences24 from the originating review. As one baseline to compare
against, we take the canonical summarization standard of extracting the first N
sentences — in general settings, authors often begin documents with an overview.
We also consider the last N sentences: in many documents, concluding material
may be a good summary, and tends to select “snippets”
from the end of movie reviews (Beineke et al., 2004). Finally, as a sanity check,
we include results from the N least subjective sentences according to Naive Bayes.
Figure 3.6 shows the polarity classifier results as N ranges between 1 and 40.
Our first observation is that the NB detector provides very good “bang for the
buck”: with subjectivity extracts containing as few as 15 sentences, accuracy is
quite close to what one gets if the entire review is used. In fact, for the NB polarity
classifier, just using the 5 most subjective sentences is almost as informative as the
Full review while containing on average only about 22% of the source reviews’
Also, it so happens that at N = 30, performance is actually slightly better than
(but statistically indistinguishable from) Full review even when the SVM default
polarity classifier is used (87.2% vs. 87.15%).25 This suggests potentially effective extraction alternatives other than using a fixed probability threshold (which
resulted in the lower accuracy of 86.4% reported above).
Furthermore, we see in Figure 3.6 that the N most-subjective-sentences method
These are the N sentences assigned the highest probability by the basic NB detector, regardless of
whether their probabilities exceed 50% and so would actually be classified as subjective by Naive Bayes.
For reviews with less than N sentences, the entire review is selected.
Note that roughly half of the documents in the polarity dataset contain more than 30 sentences
(average=32.3, standard deviation 15).
generally outperforms the other baseline summarization methods (which perhaps
suggests that sentiment summarization cannot be treated the same as topic-based
summarization, although this conjecture would need to be verified on other domains and data). It’s also interesting to observe how much better the last N
sentences are than the first N sentences; this may reflect a (hardly surprising) tendency for movie-review authors to place plot descriptions at the beginning rather
than the end of the text and conclude with overtly opinionated statements.
Incorporating context information
The previous section demonstrated the value of subjectivity detection. We now
examine whether context information, particularly regarding sentence proximity,
can further improve subjectivity extraction. As discussed in Section 3.3.2 and 3.3.5,
contextual constraints are easily incorporated via the minimum-cut formalism but
are difficult to encode into the input for standard Naive Bayes and SVMs.
Figure 3.7 shows the effect of adding in proximity information. ExtractNB+Prox
and ExtractSVM+Prox are the graph-based subjectivity detectors using Naive Bayes
and SVMs, respectively, for the individual scores; we depict the best performance
achieved by a single setting of the three proximity-related edge-weight parameters over all ten data folds26 (parameter selection was not a focus of the current
work). The two comparisons we are most interested in are ExtractNB+Prox versus
ExtractNB and ExtractSVM+Prox versus ExtractSVM .
We see that the context-aware graph-based subjectivity detectors tend to create
extracts that are more informative (statistically significant so (paired t-test) for
SVM subjectivity detectors only), although these extracts are longer than their
Parameters are chosen from T ∈ {1, 2, 3}, f (d) ∈ {1, e1−d , 1/d2 }, and c ∈ [0, 1] at intervals of 0.1.
Accuracy for subjective abstracts (def = NB)
Average accuracy
difference in accuracy
not statistically significant
indicates statistically significant
improvement in accuracy
Full Review
% of words extracted
Accuracy for subjective abstracts (def = SVM)
Average accuracy
Full Review
ExtractNB ExtractSVM+Prox
difference in accuracy
not statistically significant
indicates statistically significant
improvement in accuracy
% of words extracted
Figure 3.7: Word preservation rate vs. accuracy, NB (up) and SVMs (down) as default polarity classifiers. Also indicated are results for some statistical significance
context-blind counterparts. We note that the performance enhancements cannot
be attributed entirely to the mere inclusion of more sentences regardless of whether
they are subjective or not — one counterargument is that Full review yielded
substantially worse results for the NB default polarity classifier— and at any rate,
the graph-derived extracts are still substantially more concise than the full texts.
Now, while a bias for assigning nearby sentences to the same category seems
quite difficult to incorporate into NB and SVM subjectivity detectors, we also wish
to investigate whether our graph-based paradigm makes better use of contextual
constraints that can be encoded into the input of standard classifiers. For illustrative purposes, we consider paragraph-boundary information (looking only at SVM
subjectivity detection for simplicity’s sake).
It seems intuitively plausible that paragraph boundaries (an approximation to
discourse boundaries) loosen coherence constraints between nearby sentences. To
capture this notion for minimum-cut-based classification, we can simply reduce
the association scores for all pairs of sentences that occur in different paragraphs
by multiplying them by a cross-paragraph-boundary weight w ∈ [0, 1]. For standard classifiers, we can employ the trick of having the detector treat paragraphs,
rather than sentences, as the basic unit to be labeled. This enables the standard classifier to utilize coherence between sentences in the same paragraph; on
the other hand, it also (probably unavoidably) poses a hard constraint that all
of a paragraph’s sentences get the same label, which increases noise sensitivity.27
Our experiments reveal the graph-cut formulation as the better approach: for
both default polarity classifiers (NB and SVM), some choice of parameters (including w) for ExtractSVM+Prox yields statistically significant improvement over
For example, in the data we used, boundaries may have been missed due to malformed html.
its paragraph-unit non-graph counterpart (NB: 86.4% vs. 85.2%; SVM: 86.15%
vs. 85.45%).
Classification based on hidden Markov models
Labeling each sentence in a document as either subjective or objective can also
be treated as a sequence-labeling problem and modeled by hidden Markov models
(Rabiner, 1989).
Recall our task is to divide n items x1 , . . . , xn into two classes C1 and C2 .
These two classes can be represented as two states Q1 and Q2 in a hidden Markov
model (HMM). The emission probabilities (i.e., the probability with which each
state generates a given sentence in the sequence) can be obtained from language
models trained on the subjectivity datasets. The transition probabilities can be
determined from (unlabeled) data by optimizing the joint probability that the
sequences are generated. Once these parameters are determined, the sentences can
be labeled via the Viterbi algorithm. How does an HMM compare to the graphcut formulation we just investigated Which model is more expressive for this task?
Note that a first-order HMM can only capture relationships between adjacenct
pairs (as opposed to arbitrary pairs of nearby sentences), and is thus similar to the
graph-cut model with linear proximity function setting T = 1. In this sense, the
HMM may be seen as a special case of the graph-cut formulation. On the other
hand, our graph-cut formulation do not differentiate between transiting from the
subjective state to the objective state and the other way around. We pay the same
panelty for putting adjacent pairs into two classes, regardless of which label the
first item in the pair receives. If it turns out that one state is more “sticky” than the
other, then an HMM has the advantage in that it can easily model this difference
with different transition probabilities. However, in our preliminary experiments
using naive Bayes models to provide the emission probabilities, we did not get
improvement with HMMs over the graph-cut formulation.
We examined the relation between subjectivity detection and polarity classification, showing that subjectivity detection can compress reviews into much shorter
extracts that still retain polarity information at a level comparable to that of the
full review. In fact, for the Naive Bayes polarity classifier, the subjectivity extracts are shown to be more effective input than the originating document, which
suggests that they are not only shorter, but also “cleaner” representations of the
intended polarity.
We have also shown that employing the minimum-cut framework results in the
development of efficient algorithms for sentiment analysis. Utilizing contextual
information via this framework can lead to statistically significant improvement
in polarity-classification accuracy. Directions for future research include developing parameter-selection techniques, incorporating other sources of contextual
cues besides sentence proximity, and investigating other means for modeling such
Chapter 4
Exploiting class relationships for
sentiment categorization with
respect to rating scales1
Most prior work on the specific problem of categorizing expressly opinionated text
(including those presented in Chapter 3) has focused on the binary distinction of
positive vs. negative (Turney, 2002; Pang, Lee, and Vaithyanathan, 2002; Dave,
Lawrence, and Pennock, 2003; Yu and Hatzivassiloglou, 2003). But it is often
helpful to have more information than this binary distinction provides, especially if
one is ranking items by recommendation or comparing several reviewers’ opinions:
example applications include collaborative filtering and deciding which conference
submissions to accept.
Therefore, in this chapter we consider generalizing to finer-grained scales: rather
This chapter is based on the paper “Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales” with Lillian Lee, which appeared in the proceedings of ACL
2005 (Pang and Lee, 2005).
than just determine whether a review is “thumbs up” or not, we attempt to infer
the author’s implied numerical rating, such as “three stars” or “four stars”. Note
that this differs from identifying opinion strength (Wilson, Wiebe, and Hwa, 2004):
rants and raves have the same strength but represent opposite evaluations, and referee forms often allow one to indicate that one is very confident (high strength)
that a conference submission is mediocre (middling rating). Also, our task differs
from ranking not only because one can be given a single item to classify (as opposed to a set of items to be ordered relative to one another), but because there
are settings in which classification is harder than ranking, and vice versa.
One can apply standard n-ary classifiers or regression to this rating-inference
problem; independent work by Koppel and Schler (2005) considers such methods.
But an alternative approach that explicitly incorporates information about item
similarities together with label similarity information (for instance, “one star” is
closer to “two stars” than to “four stars”) is to think of the task as one of metric
labeling (Kleinberg and Tardos, 2002), where label relations are encoded via a
distance metric. This observation yields a meta-algorithm, applicable to both semisupervised (via graph-theoretic techniques) and supervised settings, that alters a
given n-ary classifier’s output so that similar items tend to be assigned similar
In what follows, we first demonstrate that humans can discern relatively small
differences in (hidden) evaluation scores, indicating that rating inference is indeed
a meaningful task. We then present three types of algorithms — one-vs-all, regression, and metric labeling — that can be distinguished by how explicitly they
attempt to leverage similarity between items and between labels. Next, we consider
what item similarity measure to apply, proposing one based on the positive-sentence
percentage. Incorporating this new measure within the metric-labeling framework
is shown to often provide significant improvements over the other algorithms.
We hope that some of the insights derived here might apply to other scales for
text classifcation that have been considered, such as clause-level opinion strength
(Wilson, Wiebe, and Hwa, 2004); affect types like disgust (Subasic and Huettner,
2001; Liu, Lieberman, and Selker, 2003); reading level (Collins-Thompson and
Callan, 2004); and urgency or criticality (Horvitz, Jacobs, and Hovel, 1999).
Problem validation and formulation
We first ran a small pilot study on human subjects in order to establish a rough
idea of what a reasonable classification granularity is: if even people cannot accurately infer labels with respect to a five-star scheme with half stars, say, then we
cannot expect a learning algorithm to do so. Indeed, some potential obstacles to
accurate rating inference include lack of calibration (e.g., what an understated author intends as high praise may seem lukewarm), author inconsistency at assigning
fine-grained ratings, and ratings not entirely supported by the text2 .
For data, we first collected Internet movie reviews in English from four authors,
removing explicit rating indicators from each document’s text automatically. Now,
while the obvious experiment would be to ask subjects to guess the rating that a
review represents, doing so would force us to specify a fixed rating-scale granularity
in advance. Instead, we examined people’s ability to discern relative differences,
because by varying the rating differences represented by the test instances, we can
evaluate multiple granularities in a single experiment. Specifically, at intervals
For example, the critic Dennis Schwartz writes that “sometimes the review itself [indicates] the letter
grade should have been higher or lower, as the review might fail to take into consideration my overall impression of the film — which I hope to capture in the grade” (˜ozus/cinema.htm).
Table 4.1: Human accuracy at determining relative positivity. Rating differences
are given in “notches”. Parentheses enclose the number of pairs attempted.
Rating diff.
3 or more
Subject 1
Subject 2
100% 100% (35) 100% (15)
2 (e.g., 1 star)
1 (e.g.,
77% (30) 100% (11)
65% (57)
90% (10)
47% (15)
80% ( 5)
over a number of weeks, we authors (a non-native and a native speaker of English)
examined pairs of reviews, attemping to determine whether the first review in each
pair was (1) more positive than, (2) less positive than, or (3) as positive as the
second. The texts in any particular review pair were taken from the same author
to factor out the effects of cross-author divergence.
As Table 4.1 shows, both subjects performed perfectly when the rating separation was at least 3 “notches” in the original scale (we define a notch as a half star
in a four- or five-star scheme and 10 points in a 100-point scheme). Interestingly,
although human performance drops as rating difference decreases, even at a onenotch separation, both subjects handily outperformed the random-choice baseline
of 33%. However, there was large variation in accuracy between subjects.3
Because of this variation, we defined two different classification regimes. From
the evidence above, a three-class task (categories 0, 1, and 2 — essentially “neg3
One contributing factor may be that the subjects viewed disjoint document sets, since we wanted
to maximize experimental coverage of the types of document pairs within each difference class. We thus
cannot report inter-annotator agreement, but since our goal is to recover a reviewer’s “true” recommendation, reader-author agreement is more relevant.
While another factor might be degree of English fluency, in an informal experiment (six subjects
viewing the same three pairs), native English speakers made the only two errors.
ative”, “middling”, and “positive”, respectively) seems like one that most people
would do quite well at (but we should not assume 100% human accuracy: according to our one-notch results, people may misclassify borderline cases like 2.5 stars).
Our study also suggests that people could do at least fairly well at distinguishing
full stars in a zero- to four-star scheme. However, when we began to construct
five-category datasets for each of our four authors (see below), we found that in
each case, either the most negative or the most positive class (but not both) contained only about 5% of the documents. To make the classes more balanced, we
folded these minority classes into the adjacent class, thus arriving at a four-class
problem (categories 0-3, increasing in positivity). Note that the four-class problem
seems to offer more possibilities for leveraging class relationship information than
the three-class setting, since it involves more class pairs. Also, as we discussed in
Chapter 3, even the two-category version of the rating-inference problem for movie
reviews has proven quite challenging for many automated classification techniques.
We applied the above two labeling schemes to a scale dataset4 containing
four corpora of movie reviews. All reviews were automatically pre-processed to
remove both explicit rating indicators and objective sentences; the motivation for
the latter step is that it has previously aided positive vs. negative classification (as
mentioned in Section 3.3). All of the 1770, 902, 1307, or 1027 documents in a given
corpus were written by the same author. This decision facilitates interpretation
of the results, since it factors out the effects of different choices of methods for
calibrating authors’ scales.5 We point out that it is possible to gather author4
Available at as scale dataset v1.0.
From the Rotten Tomatoes website’s FAQ: “star systems are not consistent between critics. For
critics like Roger Ebert and James Berardinelli, 2.5 stars or lower out of 4 stars is always negative. For
other critics, 2.5 stars can either be positive or negative. Even though Eric Lurio uses a 5 star system,
his grading is very relaxed. So, 2 stars can be positive.” Thus, calibration may sometimes require strong
familiarity with the authors involved, as anyone who has ever needed to reconcile conflicting referee
reports probably knows.
specific information in some practical applications: for instance, systems that use
selected authors (e.g., the Rotten Tomatoes movie-review website — where, we
note, not all authors provide explicit ratings) could require that someone submit
rating-labeled samples of newly-admitted authors’ work. Moreover, our results at
least partially generalize to mixed-author situations (see Section 4.5.2).
Recall that the problem we are considering is multi-category classification in which
the labels can be naturally mapped to a metric space (e.g., points on a line);
for simplicity, we assume the distance metric d(`, `0 ) = |` − `0 | throughout. In
this section, we present three approaches to this problem in order of increasingly
explicit use of pairwise similarity information between items and between labels.
In order to make comparisons between these methods meaningful, we base all three
of them on Support Vector Machines (SVMs) as implemented in Joachims’ (1999)
SVMlight package.
The standard SVM formulation applies only to binary classification. One-vs-all
(OVA) (Rifkin and Klautau, 2004) is a common extension to the n-ary case. Training consists of building, for each label `, an SVM binary classifier distinguishing
label ` from “not-`”. We consider the final output to be a label preference function
π ova (x, `), defined as the signed distance of (test) item x to the ` side of the ` vs.
not-` decision plane.
Clearly, OVA makes no explicit use of pairwise label or item relationships.
However, it can perform well if each class exhibits sufficiently distinct language;
see Section 4.4 for more discussion.
Alternatively, we can take a regression perspective by assuming that the labels
come from a discretization of a continuous function g mapping from the feature
space to a metric space.6 If we choose g from a family of sufficiently “gradual”
functions, then similar items necessarily receive similar labels. In particular, we
consider linear, ε-insensitive SVM regression (Vapnik, 1995; Smola and Schölkopf,
1998); the idea is to find the hyperplane that best fits the training data, but where
training points whose labels are within distance ε of the hyperplane incur no loss.
Then, for (test) instance x, the label preference function π reg (x, `) is the negative
of the distance between ` and the value predicted for x by the fitted hyperplane
Wilson, Wiebe, and Hwa (2004) used SVM regression to classify clause-level
strength of opinion, reporting that it provided lower accuracy than other methods.
However, independently of our work, Koppel and Schler (2005) found that applying linear regression to classify documents (in a different corpus than ours) with
respect to a three-point rating scale provided greater accuracy than OVA SVMs
and other algorithms.
Metric labeling
Regression implicitly encodes the “similar items, similar labels” heuristic, in that
one can restrict consideration to “gradual” functions. But we can also think of
our task as a metric labeling problem (Kleinberg and Tardos, 2002), a special case
of the maximum a posteriori estimation problem for Markov random fields, to
We discuss the ordinal regression variant in Section 4.6.
explicitly encode our desideratum. Suppose we have an initial label preference
function π(x, `), perhaps computed via one of the two methods described above.
Also, let d be a distance metric on labels, and let nnk (x) denote the k nearest
neighbors of item x according to some item-similarity function wt. Then, it is
quite natural to pose our problem as finding a mapping of instances x to labels `x
(respecting the original labels of the training instances) that minimizes
−π(x, `x ) + α
f (d(`x , `y ))wt(x, y) ,
y∈nnk (x)
where f is monotonically increasing (we chose f (d) = d unless otherwise specified)
and α is a trade-off and/or scaling parameter. (The inner summation is familiar
from work in locally-weighted learning7 (Atkeson, Moore, and Schaal, 1997).) In a
sense, we are using explicit item and label similarity information to increasingly
penalize the initial classifier as it assigns more divergent labels to similar items.
In this chapter, we only report supervised-learning experiments in which the
nearest neighbors for any given test item were drawn from the training set alone.
In such a setting, the labeling decisions for different test items are independent, so
that solving the requisite optimization problem is simple.
Aside: transduction The above formulation also allows for transductive semisupervised learning as well, in that we could allow nearest neighbors to come from
both the training and test sets. It is useful to address this case, since there are
important settings in which one has a small number of labeled reviews and a
large number of unlabeled reviews, in which case considering similarities between
unlabeled texts could prove quite helpful. In full generality, the corresponding
If we ignore the π(x, `) term, different choices of f correspond to different versions of nearest-neighbor
learning, e.g., majority-vote, weighted average of labels, or weighted median of labels.
multi-label optimization problem is intractable, but for many families of f functions (e.g., convex) there exist practical exact or approximation algorithms based
on techniques for finding minimum s-t cuts in graphs (Ishikawa and Geiger, 1998;
Boykov, Veksler, and Zabih, 1999; Ishikawa, 2003). As we discussed in Section 3.3,
a minimum-cut formulation for the binary subjective/objective distinction yielded
good results Of course, there are many other related semi-supervised learning algorithms that can be relevant; see Zhu (2005b) for a survey. In particular, as we
mentioned in Section 2.1, Goldberg and Zhu (2006) presents a graph-based algorithm that addresses the semi-supervised learning setting of this problem.
Class struggle: finding a label-correlated item-similarity
We need to specify an item similarity function wt to use the metric-labeling formulation described in Section 4.3.3. We could, as is commonly done, employ
a term-overlap-based measure such as the cosine between term-frequency-based
document vectors (henceforth “TO(cos)”). However, Table 4.2 shows that in aggregate, the vocabularies of distant classes overlap to a degree surprisingly similar
to that of the vocabularies of nearby classes. Thus, item similarity as measured
by TO(cos) may not correlate well with similarity of the item’s true labels.
We can potentially develop a more useful similarity metric by asking ourselves
what, intuitively, accounts for the label relationships that we seek to exploit. A
simple hypothesis is that ratings can be determined by the positive-sentence percentage (PSP) of a text, i.e., the number of positive sentences divided by the
number of subjective sentences. (As mentioned before, term-based versions of this
premise have motivated much sentiment-analysis work for over a decade (Das and
Table 4.2: Average over authors and class pairs of between-class vocabulary overlap
as the class labels of the pair grow farther apart.
Label difference:
Three-class data
Four-class data
Chen, 2001; Tong, 2001; Turney, 2002).) But counterexamples are easy to construct: reviews can contain off-topic opinions, or recount many positive aspects
before describing a fatal flaw.
We therefore tested the hypothesis as follows. To avoid the need to hand-label
sentences as positive or negative, we first created a sentence polarity dataset8 consisting of 10,662 movie-review “snippets” (a striking extract usually one sentence
long) downloaded from; each snippet was labeled with
its source review’s label (positive or negative) as provided by Rotten Tomatoes.
Then, we trained a Naive Bayes classifier on this data set and applied it to our
scale dataset to identify the positive sentences (recall that objective sentences were
already removed).
Figure 4.1 shows that all four authors tend to exhibit a higher PSP when they
write a more positive review, and we expect that most typical reviewers would
follow suit. Hence, PSP appears to be a promising basis for computing document
similarity for our rating-inference task. In particular, we defined PSP(x) to be
the two-dimensional vector (PSP(x), 1 − PSP(x)), and then set the item-similarity
Available at as sentence polarity dataset
function required by the metric-labeling optimization function (Section 4.3.3) to
−−−−→ −−−−→
wt(x, y) = cos PSP(x), PSP(y) .9
Statistics of PSP (positive-sentence percentage)
mean and standard deviation of PSP
Figure 4.1: Average and standard deviation of PSP for reviews expressing different
But before proceeding, we note that it is possible that similarity information
might yield no extra benefit at all. For instance, we don’t need it if we can reliably
identify each class just from some set of distinguishing terms. If we define such
terms as frequent ones (n ≥ 20) that appear in a single class 50% or more of the
time, then we do find many instances; some examples for one author are: “meaningless”, “disgusting” (class 0); “pleasant”, “uneven” (class 1); and “oscar”, “gem”
(class 2) for the three-class case, and, in the four-class case, “flat”, “tedious” (class
While admittedly we initially chose this function because it was convenient to work with cosines,
post hoc analysis revealed that the corresponding metric space “stretched” certain distances in a useful
1) versus “straightforward”, “likeable” (class 2). Some unexpected distinguishing
terms for this author are “lion” for class 2 (three-class case), and for class 2 in the
four-class case, “jennifer”, for a wide variety of Jennifers.
This section compares the accuracies of the approaches outlined in Section 4.3
on the four corpora comprising our scale dataset. (Results using L1 error were
qualitatively similar.) Throughout, when we refer to something as “significant”,
we mean statistically so with respect to the paired t-test, p < .05.
The results that follow are based on SVMlight ’s default parameter settings for
SVM regression and OVA. Preliminary analysis of the effect of varying the regression parameter ε in the four-class case revealed that the default value was often
The notation “A+B” denotes metric labeling where method A provides the
initial label preference function π and B serves as similarity measure. To train, we
first select the meta-parameters k and α by running 9-fold cross-validation within
the training set. Fixing k and α to those values yielding the best performance, we
then re-train A (but with SVM parameters fixed, as described above) on the whole
training set. At test time, the nearest neighbors of each item are also taken from
the full training set.
Main comparison
Figures 4.2 and 4.3 summarizes our average 10-fold cross-validation accuracy results. We first observe from the plots that all the algorithms described in Section
4.3 always definitively outperform the simple baseline of predicting the majority
Author a
Author b
Author c
Author d
Average ten-fold cross-validation accuracies. Open icons: SVMs in either oneversus-all (square) or regression (circle) mode; dark versions: metric labeling using
the corresponding SVM together with the positive-sentence percentage (PSP).
a b c d
a b c d
a b c d
a b c d
./. .
/// .
. .
. ..
. .
Triangles point towards significantly better algorithms for the results plotted above.
Specifically, if the difference between a row and a column algorithm for a given
author dataset (a, b, c, or d) is significant, a triangle points to the better one;
otherwise, a dot (.) is shown. Dark icons highlight the effect of adding PSP
information via metric labeling.
Figure 4.2: Results for the three-class classification task.
Author a
Author b
Author c
Author d
Average ten-fold cross-validation accuracies. Open icons: SVMs in either oneversus-all (square) or regression (circle) mode; dark versions: metric labeling using
the corresponding SVM together with the positive-sentence percentage (PSP).
a b c d
a b c d
a b c d
a b c d
// . .
/. . .
/. ./
/. . .
Triangles point towards significantly better algorithms for the results plotted above.
Specifically, if the difference between a row and a column algorithm for a given
author dataset (a, b, c, or d) is significant, a triangle points to the better one;
otherwise, a dot (.) is shown. Dark icons highlight the effect of adding PSP
information via metric labeling.
Figure 4.3: Results for the four-class classification task.
class, although the improvements are smaller in the four-class case. Incidentally,
the data was distributed in such a way that the absolute performance of the baseline itself does not change much between the three- and four-class case (which
implies that the three-class datasets were relatively more balanced); and Author
c’s datasets seem noticeably easier than the others.
We now examine the effect of implicitly using label and item similarity. In
the four-class case, regression performed better than OVA (significantly so for two
authors, as shown in the righthand table); but for the three-category task, OVA
significantly outperforms regression for all four authors. One might initially interprete this “flip” as showing that in the four-class scenario, item and label similarities provide a richer source of information relative to class-specific characteristics,
especially since for the non-majority classes there is less data available; whereas in
the three-class setting the categories are better modeled as quite distinct entities.
However, the three-class results for metric labeling on top of OVA and regression (shown in Figure 4.2 by black versions of the corresponding icons) show that
employing explicit similarities always improves results, often to a significant degree, and yields the best overall accuracies. Thus, we can in fact effectively exploit
similarities in the three-class case. Additionally, in both the three- and four- class
scenarios, metric labeling often brings the performance of the weaker base method
up to that of the stronger one (as indicated by the “disappearance” of upward
triangles in corresponding table rows), and never hurts performance significantly.
In the four-class case, metric labeling and regression seem roughly equivalent.
One possible interpretation is that the relevant structure of the problem is already
captured by linear regression (and perhaps a different kernel for regression would
have improved its three-class performance). However, according to additional ex-
periments we ran in the four-class situation, the test-set-optimal parameter settings
for metric labeling would have produced significant improvements, indicating there
may be greater potential for our framework. At any rate, we view the fact that
metric labeling performed quite well for both rating scales as a definitely positive
Further discussion
Q: Metric labeling looks like it’s just combining SVMs with nearest neighbors,
and classifier combination often improves performance. Couldn’t we get the same
kind of results by combining SVMs with any other reasonable method?
No. For example, if we take the strongest base SVM method for initial la-
bel preferences, but replace PSP with the term-overlap-based cosine (TO(cos)),
performance often drops significantly. This result, which is in accordance with
Section 4.4’s data, suggests that choosing an item similarity function that correlates well with label similarity is important. (ova+PSP //// ova+TO(cos) [3c];
reg+PSP / reg+TO(cos) [4c])
Q: Could you explain that notation, please?
Triangles point toward the significantly better algorithm for some dataset.
For instance, “M //. N [3c]” means, “In the 3-class task, method M is significantly
better than N for two author datasets and significantly worse for one dataset
(so the algorithms were statistically indistinguishable on the remaining dataset)”.
When the algorithms being compared are statistically indistinguishable on all four
datasets (the “no triangles” case), we indicate this with an equals sign (“=”).
Q: Thanks. Doesn’t Figure 4.1 show that the positive-sentence percentage would
be a good classifier even in isolation, so metric labeling isn’t necessary?
A: No. Predicting class labels directly from the PSP value via trained thresholds
isn’t as effective (ova+PSP //// threshold PSP [3c]; reg+PSP // threshold PSP [4c]).
Alternatively, we could use only the PSP component of metric labeling by
setting the label preference function to the constant function 0, but even with testset-optimal parameter settings, doing so underperforms the trained metric labeling
algorithm with access to an initial SVM classifier (ova+PSP //// 0+PSP∗ [3c];
reg+PSP // 0+PSP∗ [4c]).
Q: What about using PSP as one of the features for input to a standard classifier?
A: Our focus is on investigating the utility of similarity information. In our
particular rating-inference setting, it so happens that the basis for our pairwise
similarity measure can be incorporated as an item-specific feature, but we view
this as a tangential issue. That being said, preliminary experiments show that
metric labeling can be better, barely (for test-set-optimal parameter settings for
both algorithms: significantly better results for one author, four-class case; statistically indistinguishable otherwise), although one needs to determine an appropriate
weight for the PSP feature to get good performance.
Q: You defined the “metric transformation” function f as the identity function
f (d) = d, imposing greater loss as the distance between labels assigned to two
similar items increases. Can you do just as well if you penalize all non-equal label
assignments by the same amount, or does the distance between labels really matter?
A: You’re asking for a comparison to the Potts model, which sets f to the function
fˆ(d) = 1 if d > 0, 0 otherwise. In the one setting in which there is a significant difference between the two, the Potts model does worse (ova+PSP / ova+̂PSP [3c]).
Also, employing the Potts model generally leads to fewer significant improvements over a chosen base method (compare Figures 4.2 and 4.3’s tables with:
reg+̂PSP / reg [3c]; ova+̂PSP // ova [3c]; ova+̂PSP = ova [4c]; but note that
reg+̂PSP / reg [4c]). We note that optimizing the Potts model in the multi-label
case is NP-hard, whereas the optimal metric labeling with the identity metrictransformation function can be efficiently obtained (see Section 4.3.3).
Q: Your datasets had many labeled reviews and only one author each. Is your
work relevant to settings with many authors but very little data for each?
A: As discussed in Section 4.2, it can be quite difficult to properly calibrate
different authors’ scales, since the same number of “stars” even within what is
ostensibly the same rating system can mean different things for different authors.
But since you ask: we temporarily turned a blind eye to this serious issue, creating
a collection of 5394 reviews by 496 authors with at most 80 reviews per author,
where we pretended that our rating conversions mapped correctly into a universal
rating scheme. Preliminary results on this dataset were actually comparable to
the results reported above, although since we are not confident in the class labels
themselves, more work is needed to derive a clear analysis of this setting. (Abusing
notation, since we’re already playing fast and loose: [3c]: baseline 52.4%, reg
61.4%, reg+PSP 61.5%, ova (65.4%) . ova+PSP (66.3%); [4c]: baseline 38.8%, reg
(51.9%) . reg+PSP (52.7%), ova (53.8%) . ova+PSP (54.6%))
In future work, it would be interesting to determine author-independent characteristics that can be used on (or suitably adapted to) data for specific authors.
Q: How about trying —
A: —Yes, there are many alternatives. A few that we tested are described in
Section 4.7, and we propose some others in the next section. We should mention
that we have not yet experimented with all-vs.-all (AVA), another standard binaryto-multi-category classifier conversion method, because we wished to focus on the
effect of omitting pairwise information. In independent work on 3-category rating
inference for a different corpus, Koppel and Schler (2005) found that regression
outperformed AVA, and Rifkin and Klautau (2004) argue that in principle OVA
should do just as well as AVA.
Related work and future directions
In this chapter, we addressed the rating-inference problem, showing the utility of
employing label similarity and (appropriate choice of) item similarity — either
implicitly, through regression, or explicitly and often more effectively, through
metric labeling.
In the future, we would like to apply our methods to other scale-based classification problems, and explore alternative methods. Clearly, varying the kernel
in SVM regression might yield better results. Another choice is ordinal regression
(McCullagh, 1980; Herbrich, Graepel, and Obermayer, 2000), which only considers the ordering on labels, rather than any explicit distances between them; this
approach could work well if a good metric on labels is lacking. Also, one could use
mixture models (e.g., combine “positive” and “negative” language models) to capture class relationships (McCallum, 1999; Schapire and Singer, 2000; Takamura,
Matsumoto, and Yamada, 2004).
We are also interested in framing multi-class but non-scale-based categorization
problems as metric labeling tasks. For example, positive vs. negative vs. neutral
sentiment distinctions are sometimes considered in which neutral means either
objective (Engström, 2004) or a conflation of objective with a rating of mediocre
(Das and Chen, 2001). (Koppel and Schler (2005) in independent work also discuss
various types of neutrality.) In either case, we could apply a metric in which
positive and negative are closer to objective (or objective+mediocre) than to each
other. As another example, hierarchical label relationships can be easily encoded
in a label metric.
Finally, as mentioned in Section 4.3.3, Goldberg and Zhu (2006) address the
transductive setting, in which one has a small amount of labeled data and uses
relationships between unlabeled items; this setting would also be well-suited to the
metric-labeling approach and may be interesting to try out in the future.
Other variations attempted
Discretizing binary classification
In our setting, we can also incorporate class relations by directly altering the output
of a binary classifier, as follows. We first train a standard SVM, treating ratings
greater than 0.5 as positive labels and others as negative labels. If we then consider
the resulting classifier to output a positivity-preference function π+ (x), we can then
learn a series of thresholds to convert this value into the desired label set, under
the assumption that the bigger π+ (x) is, the more positive the review.10 This
algorithm always outperforms the majority-class baseline, but not to the degree
that the best of SVM OVA and SVM regression does. Koppel and Schler (2005)
independently found in a three-class study that thresholding a positive/negative
classifier trained only on clearly positive or clearly negative examples did not yield
This is not necessarily true: if the classifier’s goal is to optimize binary classification error, its major
concern is to increase confidence in the positive/negative distinction, which may not correspond to higher
confidence in separating “five stars” from “four stars”.
large improvements.
Discretizing regression
In our experiments with SVM regression, we discretized regression output via a
set of fixed decision thresholds {0.5, 1.5, 2.5, ...} to map it into our set of class
labels. Alternatively, we can learn the thresholds instead. Neither option clearly
outperforms the other in the four-class case. In the three-class setting, the learned
version provides noticeably better performance in two of the four datasets. But
these results taken together still mean that in many cases, the difference is negligible, and if we had started down this path, we would have needed to consider
similar tweaks for one-vs-all SVM as well. We therefore stuck with the simpler
version in order to maintain focus on the central issues at hand.
Chapter 5
Variation on the theme of polarity
and document relationship with
politically oriented text1
Introduction: determining support or opposition from
Congressional floor-debate transcripts
One ought to recognize that the present political chaos is connected with
the decay of language, and that one can probably bring about some improvement by starting at the verbal end.
— Orwell, Politics and the English language
We have entered an era where very large amounts of politically oriented text are
now available online. This includes both official documents, such as the full text
of laws and the proceedings of legislative bodies, and unofficial documents, such as
This chapter is based on the paper “Get out the vote: Determining support or opposition from
congressional floor-debate transcripts” with Matt Thomas and Lillian Lee, which will appear in the
proceedings of EMNLP 2006 (Thomas, Pang, and Lee, 2006).
postings on weblogs (blogs) devoted to politics. In some sense, the availability of
such data is simply a manifestation of a general trend of “everybody putting their
records on the Internet”.2 The online accessibility of politically oriented texts in
particular, however, is a phenomenon that some have gone so far as to say will
have a potentially society-changing effect.
In the United States, for example, governmental bodies are providing and soliciting political documents via the Internet, with lofty goals in mind: electronic
rulemaking (eRulemaking) initiatives involving the “electronic collection, distribution, synthesis, and analysis of public commentary in the regulatory rulemaking
process”, may “[alter] the citizen-government relationship” (Shulman and Schlosberg, 2002). Additionally, much media attention has been focused recently on the
potential impact that Internet sites may have on politics3 , or at least on political
journalism4 . Regardless of whether one views such claims as clear-sighted prophecy
or mere hype, it is obviously important to help people understand and analyze politically oriented text, given the importance of enabling informed participation in
the political process.
Evaluative and persuasive documents, such as a politician’s speech regarding
a bill or a blogger’s commentary on a legislative proposal, form a particularly
interesting type of politically oriented text. People are much more likely to consult
such evaluative statements than the actual text of a bill or law under discussion,
given the dense nature of legislative language and the fact that (U.S.) bills often
reach several hundred pages in length (Smith, Roberts, and Vander Wielen, 2005).
It is worth pointing out that the United States’ Library of Congress was an extremely early adopter
of Web technology: the THOMAS database ( of congressional bills and related
data was launched in January 1995, when Mosaic was not quite two years old and Altavista did not yet
E.g., “Internet injects sweeping change into U.S. politics”, Adam Nagourney, The New York Times,
April 2, 2006.
E.g., “The End of News?”, Michael Massing, The New York Review of Books, December 1, 2005.
Moreover, political opinions are explicitly solicited in the eRulemaking scenario.
In the analysis of evaluative language, it is fundamentally necessary to determine whether the author/speaker supports or disapproves of the topic of discussion. In this chapter, we investigate the following specific instantiation of this
problem: we seek to determine from the transcripts of U.S. Congressional floor
debates whether each “speech” (continuous single-speaker segment of text) represents support for or opposition to a proposed piece of legislation. Note that from
an experimental point of view, this is a very convenient problem to work with
because we can automatically determine ground truth (and thus avoid the need
for manual annotation) simply by consulting publicly available voting records.
Task properties Determining whether or not a speaker supports a proposal
again falls within the realm of sentiment analysis. In particular, since we treat
each individual speech within a debate as a single “document”, we are considering
a version of sentiment-polarity classification discussed in Chapter 3.
Most sentiment-polarity classifiers proposed in the recent literature categorize
each document independently. A few others, including our own work described
in Chapter 4 incorporate various measures of inter-document similarity between
the texts to be labeled (Agarwal and Bhattacharyya, 2005; Pang and Lee, 2005;
Goldberg and Zhu, 2006). Many interesting opinion-oriented documents, however,
can be linked through certain relationships that occur in the context of evaluative
discussions. For example, we may find textual5 evidence of a high likelihood of
Because we are most interested in techniques applicable across domains, we restrict consideration
to NLP aspects of the problem, ignoring external problem-specific information. For example, although
most votes in our corpus were almost completely along party lines (and despite the fact that sameparty information is easily incorporated via the methods we propose), we did not use party-affiliation
data. Indeed, in other settings (e.g., a movie-discussion listserv) one may not be able to determine the
participants’ political leanings, and such information may not lead to significantly improved results even
if it were available.
agreement between two speakers, such as explicit assertions (“I second that!”) or
quotation of messages in emails or postings (see Mullen and Malouf (2006) but cf.
Agrawal et al. (2003), who observed responses in newsgroups are more likely to
antagonistic than reinforcing). Agreement evidence can be a powerful aid in our
classification task: for example, we can easily categorize a complicated (or overly
terse) document if we find within it indications of agreement with a clearly positive
Obviously, incorporating agreement information provides additional benefit
only when the input documents are relatively difficult to classify individually. Intuition suggests that this is true of the data with which we experiment, for several
reasons. First, U.S. congressional debates contain very rich language and cover an
extremely wide variety of topics, ranging from flag burning to international policy
to the federal budget. Debates are also subject to digressions, some fairly natural
and others less so (e.g., “Why are we discussing this bill when the plight of my
constituents regarding this other issue is being ignored?”)
Second, an important characteristic of persuasive language is that speakers may
spend more time presenting evidence in support of their positions (or attacking the
evidence presented by others) than directly stating their attitudes. An extreme
example will illustrate the problems involved. Consider a speech that describes the
U.S. flag as deeply inspirational, and thus contains only positive language. If the
bill under discussion is a proposed flag-burning ban, then the speech is supportive;
but if the bill under discussion is aimed at rescinding an existing flag-burning ban,
the speech may represent opposition to the legislation. Given the current state of
the art in sentiment analysis, it is doubtful that one could determine the (probably
topic-specific) relationship between presented evidence and speaker opinion.
Table 5.1: Corpus statistics.
total train
average number of speech segments per debate
average number of speakers per debate
speech segments
Qualitative summary of results The above difficulties underscore the importance of enhancing standard classification techniques with new information sources
that promise to improve accuracy, such as inter-document relationships between
the documents to be labeled. In this work, we demonstrate that the incorporation
of agreement modeling can provide substantial improvements over the application
of support vector machines (SVMs) in isolation, which represents the state of the
art in the individual classification of documents. The enhanced accuracies are
obtained via a fairly primitive automatically-acquired “agreement detector” and
a conceptually simple method for integrating isolated-document and agreementbased information. We thus view our results as demonstrating the potentially
large benefits of exploiting sentiment-related discourse-segment relationships in
sentiment-analysis tasks.
Corpus: transcripts of U.S. floor debates
This section outlines the main steps of the process by which we created our corpus
(download site:
GovTrack ( is an independent website run by Joshua Tauberer
that collects publicly available data on the legislative and fund-raising activities
of U.S. congresspeople. Due to its extensive cross-referencing and collating of information, it was nominated for a 2006 “Webby” award. A crucial characteristic
of GovTrack from our point of view is that the information is provided in a very
convenient format; for instance, the floor-debate transcripts are broken into separate HTML files according to the subject of the debate, so we can trivially derive
long sequences of speeches guaranteed to cover the same topic.
We extracted from GovTrack all available transcripts of U.S. floor debates in
the House of Representatives for the year 2005 (3268 pages of transcripts in total),
together with voting records for all roll-call votes during that year. We concentrated on debates regarding “controversial” bills (ones in which the losing side
generated at least 20% of the speeches) because these debates should presumably
exhibit more interesting discourse structure.
Each debate consists of a series of speech segments, where each segment is a
sequence of uninterrupted utterances by a single speaker. Since speech segments
represent natural discourse units, we treat them as the basic unit to be classified.
Each speech segment was labeled by the vote (“yea” or “nay”) cast for the proposed
bill by the person who uttered the speech segment.
We automatically discarded those speech segments belonging to a class of formulaic, generally one-sentence utterances focused on the yielding of time on the
house floor (for example, “Madam Speaker, I am pleased to yield 5 minutes to the
gentleman from Massachusetts”), as such speech segments are clearly off-topic. We
also removed speech segments containing the term “amendment”, since we found
during initial inspection that these speeches generally reflect a speaker’s opinion
on an amendment, and this opinion may differ from the speaker’s opinion on the
underlying bill under discussion.
We randomly split the data into training, test, and development (parametertuning) sets representing roughly 70%, 20%, and 10% of our data, respectively (see
Table 5.1). The speech segments remained grouped by debate, with 38 debates
assigned to the training set, 10 to the test set, and 5 to the development set; we
require that the speech segments from an individual debate all appear in the same
set because our goal is to examine classification of speech segments in the context
of the surrounding discussion.
The support/oppose classification problem can be approached through the use of
standard classifiers such as support vector machines (SVMs), which consider each
text unit in isolation. As discussed in Section 5.1, however, the conversational nature of our data implies the existence of various relationships that can be exploited
to improve cumulative classification accuracy for speech segments belonging to the
same debate. We adopt the same graph-cut formulation described in Section 3.3
as our classification framework, which integrates both perspectives, optimizing its
labeling of speech segments based on both individual speech-segment classification
scores and preferences for groups of speech segments to receive the same label.
More specifically, let s1 , s2 , . . . , sn be the sequence of speech segments within
a given debate, and let Y and N stand for the “yea” and “nay” class, respectively. Assume we have a non-negative function ind(s, C) indicating the degree of
preference that an individual-document classifier, such as an SVM, has for placing
speech-segment s in class C. Also, assume that some pairs of speech segments have
weighted links between them, where the non-negative strength (weight) str(`) for a
link ` indicates the degree to which it is preferable that the linked speech segments
receive the same label. Then, any class assignment c = c(s1 ), c(s2 ), . . . , c(sn ) can
be assigned a cost
ind(s, c(s)) +
s,s0 :
c(s)6=c(s0 )
between s,s0
where c(s) is the “opposite” class from c(s). Again, a minimum-cost assignment
represents an optimum way to classify the speech segments so that each one tends
not to be put into the class that the individual-document classifier disprefers, but
at the same time, highly associated speech segments tend not to be put in different
The above optimization function and the advantage of using this framework
has been discussed in Section 3.3. The contribution of our work in this chapter
is the examination of new types of relationships, not the method by which such
relationships are incorporated into the classification decision.
Classifying speech segments in isolation
In our experiments, we employed the well-known classifier SV M light to obtain
individual-document classification scores, treating Y as the positive class and using plain unigrams as features.6 Following standard practice in sentiment analysis
(Pang, Lee, and Vaithyanathan, 2002), the input to SV M light consisted of normalized presence-of-feature (rather than frequency-of-feature) vectors7 . The ind value
for each speech segment s was based on the signed distance d(s) from the vector
SV M light is available at Default parameters were used, although experimentation with different parameter settings is an important direction for future work (Daelemans and Hoste,
2002; Munson, Cardie, and Caruana, 2005).
Note that this was in fact suboptimal in some settings for this particular dataset
representing s to the trained SVM decision plane:
d(s) > 2σs ;
ind(s, Y) =
/2 |d(s)| ≤ 2σs ;
d(s) < −2σs
where σs is the standard deviation of d(s) over all speech segments s in the debate
in question, and ind(s, N ) = 1 − ind(s, Y). This way, any d(s) between −2σs and
2σs (for normally distributed data, this should cover 95% of the population) is
normalized into an ind score between 0 and 1.
We now turn to the more interesting problem of representing the preferences
that speech segments may have for being assigned to the same class.
Relationships between speech segments
A wide range of relationships between text segments can be modeled as positivestrength links. Here we discuss two types of constraints that are considered in this
Same-speaker constraints: In Congressional debates and in general socialdiscourse contexts, a single speaker may make a number of comments regarding
a topic. It is reasonable to expect that in many settings, the participants in a
discussion may be convinced to change their opinions midway through a debate.
Hence, in the general case we wish to be able to express “soft” preferences for all
of an author’s statements to receive the same label, where the strengths of such
constraints could, for instance, vary according to the time elapsed between the
statements. Weighted links are an appropriate means to express such variation.
However, if we assume that most speakers do not change their positions in
the course of a discussion, we can conclude that all comments made by the same
speaker must receive the same label. This assumption holds by fiat for the groundtruth labels in our dataset because these labels were derived from the single vote
cast by the speaker on the bill being discussed.8 We can implement this assumption
via links whose weights are essentially infinite. Although one can also implement
this assumption via concatenation of same-speaker speech segments (see Section
5.4.3), we view the fact that our graph-based framework incorporates both hard
and soft constraints in a principled fashion as an advantage of our approach.
Different-speaker agreements In House discourse, it is common for one speaker
to make reference to another in the context of an agreement or disagreement over
the topic of discussion. The systematic identification of instances of agreement
can, as we have discussed, be a powerful tool for the development of intelligently
selected weights for links between speech segments.
The problem of agreement identification can be decomposed into two subproblems: identifying references and their targets, and deciding whether each
reference represents an instance of agreement. In our case, the first task is straightforward because we focused solely on by-name references. Members of Congress
typically refer to each other by formulaic structures such as “the gentleman from
Ohio”, and House transcripts accompany each such reference with the last name
of the reference target (e.g. “Mr. Smith”) in parentheses. To isolate references
in our implementation, we examine all statements of names beginning with Mr.,
We are attempting to determine whether a speech segment represents support or not. This differs
from the problem of determining what the speaker’s actual opinion is, a problem that, as an anonymous
reviewer put it, is complicated by “grandstanding, backroom deals, or, more innocently, plain change of
mind (‘I voted for it before I voted against it’)”.
Ms., or Mrs.,9 and we attempt to match each such reference with a House member
using a directory obtained from Govtrack. In cases in which multiple members
could potentially match a reference based on a common last name, we give priority to members who spoke in close proximity to the speech segment containing the
Our second, and more interesting, task with regard to reference processing is to
decide whether a given reference indicates agreement. We approach the problem
of classifying references by representing each reference with a word-presence vector
derived from a window of text surrounding the reference.10 In the training set, we
classify each reference connecting two speakers with a positive or negative label
depending on whether the two voted the same way on the bill under discussion11 .
These labels are then used to train an SVM classifier, the output of which is
subsequently used to create weights on agreement links in the test set as follows.
Let d(r) denote the distance from the vector representing reference r to the
agreement-detector SVM’s decision plane, and let σr be the standard deviation of
d(r) over all references in the debate in question. We then define the strength agr
of the agreement link corresponding to the
agr (r) = α · d(r)/4σr
reference as:
d(r) < θagr ;
θagr ≤ d(r) ≤ 4σr ;
d(r) > 4σr .
The free parameter α specifies the relative importance of the agr scores. The
One subtlety is that for the purposes of mining agreement cues (but not for evaluating overall
support/oppose classification accuracy), we temporarily re-inserted into our dataset previously filtered
speech segments containing the term “yield”, since the yielding of time on the House floor typically
indicates agreement even though the yield statements contain little relevant text on their own.
We found good development-set performance using the 30 tokens before, 20 tokens after, and the
name itself.
Since we are concerned with references that potentially represent relationships between speech segments, we ignore references for which the target of the reference did not speak in the debate in which
the reference was made.
threshold θagr controls the precision of the agreement links, in that values of θagr
greater than zero mean that greater confidence is required before an agreement
link can be added.12
This section presents experiments testing the utility of using speech-segment relationships, evaluating against a number of baselines. All reported results use values
for the free parameter α derived via tuning on the development set. In the tables, boldface indicates the development- and test-set results for the developmentset-optimal parameter settings, as one would make algorithmic choices based on
development-set performance.
Preliminaries: Reference classification
Recall that to gather inter-speaker agreement information, the strategy employed
in this work is to classify by-name references to other speakers as to whether they
indicate agreement or not.
To train our agreement classifier, we experimented with undoing the deletion
of amendment-related speech segments in the training set. Note that such speech
segments were never included in the development or test set, since, as discussed
in Section 5.2, their labels are probably noisy; however, including them in the
training set allows the classifier to examine more instances even though some of
them are labeled incorrectly. As Table 5.2 shows, using more, if noisy, data yields
better agreement-classification results on the development set, and so we use that
Our implementation puts a link between just one arbitrary pair of speech segments among all those
uttered by a given pair of apparently agreeing speakers. The “infinite-weight” same-speaker links propagate the agreement information to all other such pairs.
Table 5.2: Agreement-classifier accuracy, in percent. “Amendments”=“speech segments containing the word ‘amendment’”. Recall that boldface indicates results
for development-set-optimal settings.
Agreement classifier
Devel. set
Test set
majority baseline
Train: no amendments; θagr = 0
Train: with amendments; θagr = 0
policy in all subsequent experiments.13
An important observation is that precision may be more important than accuracy in deciding which agreement links to add: false positives with respect to
agreement can cause speech segments to be incorrectly assigned the same label,
whereas false negatives mean only that agreement-based information about other
speech segments is not employed. As described above, we can raise agreement
precision by increasing the threshold θagr , which specifies the required confidence
for the addition of an agreement link. Indeed, Table 5.3 shows that we can improve agreement precision by setting θagr to the (positive) mean agreement score
µ assigned by the SVM agreement-classifier over all references in the given debate14 . However, this comes at the cost of greatly reducing agreement accuracy
(development: 64.38%; test: 66.18%) due to lowered recall levels. Whether or not
better speech-segment classification is ultimately achieved is discussed in the next
Unfortunately, this policy leads to inferior test-set agreement classification. Section 5.4.5 contains
further discussion.
We elected not to explicitly tune the value of θagr in order to minimize the number of free parameters
to deal with.
Table 5.3: Agreement-classifier precision.
Agreement classifier Precision (in percent):
Devel. set
Test set
θagr = 0
θagr = µ
Table 5.4: Segment-based speech-segment classification accuracy, in percent.
Support/oppose classifer
Devel. set
Test set
majority baseline
#(“support”) − #(“oppos”)
SVM [speech segment]
SVM + same-speaker links
SVM + same-speaker links + agreement links, θagr = 0
SVM + same-speaker links + agreement links, θagr = µ
Segment-based speech-segment classification
Baselines The first two data rows of Table 5.4 depict baseline performance results. The #(“support”) − #(“oppos”) baseline is meant to explore whether the
speech-segment classification task can be reduced to simple lexical checks. Specifically, this method uses the signed difference between the number of words containing the stem “support” and the number of words containing the stem “oppos”
(returning the majority class if the difference is 0). No better than 62.67% test-set
accuracy is obtained by either baseline.
Table 5.5: Speaker-based speech-segment classification accuracy, in percent. Here,
the initial SVM is run on the concatenation of all of a given speaker’s speech
segments, but the results are computed over speech segments (not speakers), so
that they can be compared to those in Table 5.4.
Support/oppose classifer
Devel. set
Test set
SVM [speaker]
SVM + agreement links, θagr = 0
SVM + agreement links, θagr = µ
Using relationship information Applying an SVM to classify each speech
segment in isolation leads to clear improvements over the two baseline methods,
as demonstrated in Table 5.4. When we impose the constraint that all speech
segments uttered by the same speaker receive the same label via “same-speaker
links”, both test-set and development-set accuracy increase even more, in the latter
case quite substantially so.
The last two lines of Table 5.4 show that the best results are obtained by
incorporating agreement information as well. The highest test-set result, 71.16%,
is obtained by using a high-precision threshold to determine which agreement links
to add. While the development-set results would induce us to utilize the standard
threshold value of 0, which is sub-optimal on the test set, the θagr = 0 agreementlink policy still achieves noticeable improvement over not using agreement links
(test set: 70.81% vs. 67.21%).
Speaker-based speech-segment classification
We use speech segments as the unit of classification because they represent natural
discourse units. As a consequence, we are able to exploit relationships at the
speech-segment level. However, it is interesting to consider whether we really
need to consider relationships specifically between speech segments themselves, or
whether it suffices to simply consider relationships between the speakers of the
speech segments. In particular, as an alternative to using same-speaker links, we
tried a speaker-based approach wherein the way we determine the initial individualdocument classification score for each speech segment uttered by a person p in a
given debate is to run an SVM on the concatenation of all of p’s speech segments
within that debate. (We also ensure that agreement-link information is propagated
from speech-segment to speaker pairs.)
How does the use of same-speaker links compare to the concatenation of each
speaker’s speech segments? Tables 5.4 and 5.5 show that, not surprisingly, the
SVM individual-document classifier works better on the concatenated speech segments than on the speech segments in isolation. However, the effect on overall
classification accuracy is less clear: the development set favors same-speaker links
over concatenation, while the test set does not.
But we stress that the most important observation we can make from Table
5.5 is that once again, the addition of agreement information leads to substantial
improvements in accuracy.
“Hard” agreement constraints
Recall that in in our experiments, we created finite-weight agreement links, so that
speech segments appearing in pairs flagged by our (imperfect) agreement detector
Table 5.6: Speech-segment classification accuracy with hard agreement constraints.
We force speech segments with agreement links between them to receive the same
label, either through using infinite-weight for agreement links (for both segmentbased formulation and speaker-based formulation) or through a concatenation
strategy similar to that described in the previous subsection.
Support/oppose classifer
Devel. set
Test set
Graph with infinite-weight links
θagr = µ
Segment-based (cf. Table 5.4)
θagr = µ
Graph with infinite-weight links
θagr = 0
Speaker-based (cf. Table 5.5)
θagr = µ
θagr = 0
(cf. Table 5.4 and 5.5)
θagr = µ
can potentially receive different labels. We also experimented with forcing such
speech segments to receive the same label, either through infinite-weight agreement links or through a speech-segment concatenation strategy similar to that
described in the previous subsection. Both strategies resulted in clear degradation
in performance on both the development and test sets, a finding that validates our
encoding of agreement information as “soft” preferences.
On the development/test set split
We have seen several cases in which the method that performs best on the development set does not yield the best test-set performance. Also, in retrospect, the
size of the development set may be too small. However, we felt that it would be
illegitimate to change the train/development/test sets in a post hoc fashion, that
is, after seeing the experimental results.
Moreover, and crucially, it is very clear that using agreement information, encoded as preferences within our graph-based approach rather than as hard constraints, yields substantial improvements on both the development and test set;
this, we believe, is our most important finding.
Related work
Politically-oriented text Sentiment analysis has specifically been proposed as
a key enabling technology in eRulemaking, allowing the automatic analysis of
the opinions that people submit (Shulman et al., 2005; Cardie et al., 2006; Kwon,
Shulman, and Hovy, 2006). There has also been work focused upon determining the
political leaning (e.g., “liberal” vs. “conservative”) of a document or author, where
most previously-proposed methods make no direct use of relationships between the
documents to be classified (the “unlabeled” texts) (Laver, Benoit, and Garry, 2003;
Efron, 2004; Mullen and Malouf, 2006). An exception is Grefenstette et al. (2004),
who experimented with determining the political orientation of websites essentially
by classifying the concatenation of all the documents found on that site.
Others have applied the NLP technologies of near-duplicate detection and topicbased text categorization to politically oriented text (Yang and Callan, 2005; Purpura and Hillard, 2006).
Detecting agreement We used a simple method to learn to identify crossspeaker references indicating agreement. More sophisticated approaches have been
proposed (Hillard, Ostendorf, and Shriberg, 2003), including an extension that, in
an interesting reversal of our problem, makes use of sentiment-polarity indicators
within speech segments (adjectives with positive or negative polarity) to help derive the agreement information (Galley et al., 2004)15 . Also relevant is work on
the general problems of dialog-act tagging (Stolcke et al., 2000), citation analysis
(Lehnert, Cardie, and Riloff, 1990), and computational rhetorical analysis (Marcu,
2000; Teufel and Moens, 2002).
We currently do not have an efficient means to encode disagreement information
as hard constraints; we plan to investigate incorporating such information in future
Relationships between the unlabeled items Carvalho and Cohen (2005) consider sequential relations between different types of emails (e.g., between requests
and satisfactions thereof) to classify messages, and thus also explicitly exploit the
structure of conversations.
Previous sentiment-analysis work in different domains has considered interdocument similarity (Agarwal and Bhattacharyya, 2005; Pang and Lee, 2005; Goldberg and Zhu, 2006) or explicit inter-document references in the form of hyperlinks
(Agrawal et al., 2003).
Conclusion and future work
In this study, we focused on very general types of cross-document classification preferences, utilizing constraints based only on speaker identity and on direct textual
references between statements. We showed that the integration of even very limited information regarding inter-document relationships can significantly increase
Note that Galley et al. (2004) work with actual speech signals in the context of conversational interacation. They incorporated a number of non-texual features (such as utterance length) as well as
manually-selected lexical items in their agreement classifier.
the accuracy of support/opposition classification.
The simple constraints modeled in our study, however, represent just a small
portion of the rich network of relationships that connect statements and speakers
across the political universe and in the wider realm of opinionated social discourse.
One intriguing possibility is to take advantage of (readily identifiable) information
regarding interpersonal relationships, making use of speaker/author affiliations,
positions within a social hierarchy, and so on. Or, we could even attempt to model
relationships between topics or concepts, in a kind of extension of collaborative
filtering. For example, perhaps we could infer that two speakers sharing a common
opinion on evolutionary biologist Richard Dawkins (a.k.a. “Darwin’s rottweiler”)
will be likely to agree in a debate centered on Intelligent Design. While such
functionality is well beyond the scope of our current study, we are optimistic that
we can develop methods to exploit additional types of relationships in future work.
Chapter 6
Rhapsody on the theme of
subjectivity: collective
document-level subjectivity
detection without training data1
There never was in the world two opinions alike, no more than two
hairs, or two grains; the most universal quality is diversity.
— Montaigne, Essays
Where an opinion is general, it is usually correct.
— Austen, Mansfield Park
What other people think has always been an important piece of information for
most of us during the decision-making process. Long before awareness of the World
This chapter is based on an earlier version of the work described in “Using very simple statistics
for review search: An exploration” with Lillian Lee, which appeared in the proceedings of COLING
(Companion volume: Posters) (Pang and Lee, 2008).
Wide Web became widespread, many of us asked our friends to recommend an
auto mechanic, requested reference letters from colleagues, or consulted Consumer
Reports to decide what dishwasher to buy. Interestingly, the Web has (among
other things) made it possible to find out about the opinions and experiences of
those in the vast pool of people that are neither our personal acquaintances nor
well-known professional critics — that is, people we have never heard of.
Websites that aggregate and organize solicited reviews, such as,, and, have proven to be highly valuable sources of
information. But a system that could proactively search the Web for reviews of a
given item offers many benefits as well. Such a system could be used to augment
the contents of websites like those just mentioned. Or, users could employ such a
system directly, especially if it were integrated into their search engine of choice.
Indeed, users might find this capability a useful alternative to consulting aggregation sites. Specifically, while the element of trust might be lost (many aggregation
sites have some way to indicate the apparent quality of reviewers), what can be
gained is access to potentially many more reviews from a wider variety of people
(or from a variety of review aggregators), and the ability to find reviews on topics
not well covered by known review sites.
It might seem that one can already use typical search engines in this manner
via suitably worded queries; for instance, one can add the term “review” to the
list of query keywords. But this is an imperfect solution. For instance, at the
time of writing, querying Google for “Novotel Sydney review” brings up a number
of relevant pages, but also pages containing versions of the dreaded “This hotel
hasn’t been reviewed yet. Be the first to review it”.2 Heuristics such as looking
The hotel’s Web policy was also retrieved: “The Hotel does not review or monitor any web sites
linked to the Site” (emphasis added).
for a rating indication (e.g., “two stars”) might help, but given the potentially
wide range of site-specific rating indicators, we do not expect this to be a general
Instead, one approach would be to apply document-level subjectivity classification to the initial set of search engine results, either to filter out non-reviews
completely or simply to distinguish the subjective from the objective documents.
However, although the input consists of a collection of documents more or less on
the same focused topic (having been generated in response to a specific query),
it is impossible to determine a priori what the set of possible topics could be or
what sources the documents will be drawn from.3 This poses a serious problem for
existing techniques. As we discussed in Chapter 2, supervised sentiment-analysis
technologies have been shown to be sensitive to changes in domain or even different time-slices of the same domain (Dave, Lawrence, and Pennock, 2003; Aue
and Gamon, 2005b; Finn and Kushmerick, 2006; Read, 2005). Furthermore, even
some unsupervised methods, which generally rely on a set of typically subjectivityindicative words or phrases (Wiebe et al., 2004), exhibit quite different accuracies
for different domains, at least with respect to related sentiment-analysis classification problems (Turney, 2002); also, an unsupervised method relying solely on very
general cues ignores potentially strong evidence that can be drawn from the topicspecific documents at hand — for this reason, Dave, Lawrence, and Pennock (2003)
specifically suggest that a “specialized genre classifier” is needed for mining opinions. What we really desire, then, is a method for unsupervised, “blank-slate”
subjectivity classification, where we assume access neither to labeled data nor to
prior linguistic information regarding subjectivity indicators, but rather draw our
Recall from Chapter 2, performance results for certain within-genre distinctions, such as separating
editorials from non-opinion newswire, can be quite high (Yu and Hatzivassiloglou, 2003; Wiebe et al.,
inferences almost entirely4 from the test data at hand.
To develop such a method, we consider the following simple hypothesis: information that is repeated across the vast majority of the input documents, such
as “100 Murray Street” in the case of the Novotel Sydney query, is likely to be
factual, whereas opinions exhibit a higher degree of variance. If the hypothesis is
true, it suggests a general strategy of classifying documents as non-reviews when
their contents tend to be repeated across the input document set. Of course, the
hypothesis might be false; for instance, perhaps opinion-expressing text exhibits
less variety than has previously been conjectured5 or there is general consensus on
the quality of the item under discussion.6 But even if reviews tend to be more
alike than non-reviews, the strategy outlined above would still be workable — we
would only need to flip the objective/subjective labels. So the question is really
whether reviews and non-reviews can be distinguished by some sort of similaritybased criterion.
In this chapter, we present three different types of “blank slate” methods for
instantiating the intuition outlined above. The first approach, which clusters the
documents to be categorized, and so incorporates the similarity of specific pairs
of documents, can be considered as a simple baseline. The second checks within
each document for material that appears in other documents. The third combines
these two approaches. We find that utilizing term-distribution data across the
set of documents to be classified yields quite substantial improvements over our
baselines and very high accuracy overall, and that this information can sometimes
prove useful in rather surprising ways.
We also employ a BNC-derived term-frequency list.
David and Pinch (2005) study the phenomenon of review plagiarism on sites such as Amazon.
It is also conceivable that we might encounter the Rashômon scenario in which different authors have
their own versions of the “facts”, but further discussion of such a phenomenon lies beyond the scope of
this work.
Related work
There have been a number of supervised and unsupervised approaches to documentlevel subjectivity classification proposed in previous work (Dave, Lawrence, and
Pennock, 2003; Yu and Hatzivassiloglou, 2003; Wiebe et al., 2004; Finn and Kushmerick, 2006). To our knowledge, there has not been prior sentiment analysis
research on using the documents to be classified as the only information source
The subjectivity classification problem overlaps somewhat with the area of
genre classification (Karlgren and Cutting, 1994; Kessler, Nunberg, and Schütze,
1997; Argamon-Engelson, Koppel, and Avneri, 1998a); for instance, sometimes one
of the genre classes corresponds to editorials (which are subjective). There is also
a connection to detecting inflammatory messages, or “flames” (Spertus, 1997).
Turney (2002) proposed a knowledge-lean unsupervised approach to a sentimentanalysis problem that is related to subjectivity classification, namely, that of determining whether a subjective document conveys a positive or a negative opinion. The idea was to find words that are generally strongly associated with positive or negative sentiment by checking whether they tended to occur on the Web
near the words “poor” or “excellent”. Agarwal and Bhattacharyya (2005) give a
knowledge-rich transductive algorithm for the same problem.
Unsupervised subjectivity classification of within-document items, such as terms,
has been an active line of research; Wiebe et al. (2004) provides a survey. At the
sentence level, transductive learning has also been employed (Pang and Lee, 2004).
In the area of ad hoc information retrieval, several methods have been proposed
that seek to exploit similarities within the set of retrieved documents (Hearst and
Pedersen, 1996; Liu and Croft, 2004; Kurland and Lee, 2005; Diaz, 2005). When
the goal is to improve the rankings of the (unknown) relevant documents within the
retrieved set, taking into account each document’s similarity to the (topic-based)
query is very important to achieving good results (Liu and Croft, 2004; Kurland
and Lee, 2005; Diaz, 2005), but it is not clear that doing so makes sense in the
subjectivity-classification setting, where the query probably cannot be considered
to be itself subjective.
The idea of identifying commonalities among a group of documents has been
considered in the past by work on information fusion in the context of multidocument text summarization (Barzilay, McKeown, and Elhadad, 1999). The goal
is to identify and eliminate redundancies in the multiple information sources, and
thus enable the generation of more fluent summaries. In contrast, our interest is
in utilizing cross-document redundancy as a way to characterize individual documents.
We constructed a dataset intended to represent the core of the problem we are
interested in, namely, separating texts on a relatively focused topic into those that
are subjective and those that are objective. We chose not to work directly with
search engine results in order to avoid a number of problems, both methodological
(e.g., how should we develop a set of representative queries?) and pragmatic (e.g.,
how can we pre-process webpages to discard extraneous portions such as banner
ads, and who is going to assign subjectivity labels if a large amount of data is
collected?). Rather, we first started with the “Pool of 27886 unprocessed html files”
dataset7 released with the movie review data (described in Section 3.1.1), since the
Available at .
constituent documents (a) are known to be subjective, being movie reviews, and so
do not require manual annotation, (b) overlap, in that there can be several reviews
for the same movie, and (c) have been used by a number of groups for sentiment
analysis research. From this corpus, we selected all movies for which (i) at least
five reviews could be found in the Pool dataset, and (ii) plot summaries could be
extracted from the Internet Movie Database and/or Yahoo! Movies. Thus, each
movie represents a search topic, and the reviews and plot summaries for that movie
— which we collectively term a search set — represent subjective and objective
documents on the corresponding search topic, respectively.
Our corpus consists of a development set containing 100 search sets and a
test set containing 1353 search sets. Table 6.1 gives some descriptive statistics.
Note that the predominance of subjective documents reflects the fact that we are
simulating the results of a query like “Novotel Sydney review”.
Table 6.1: Corpus statistics, on a per-search-set basis.
# of subjective docs
# of objective docs
While ideally we would use a dataset with wider topic coverage, we believe
our corpus still serves as a reasonable simulation of the “true” search-engine setting. At any rate, we are not aware of an alternative non-labor-intensive method
for constructing a broader-coverage annotated subjectivity-classification corpus of
comparable size (23,315 documents). Section 6.4.3 discusses other characteristics
of our dataset that could impact the methods proposed in the next section.
Recall our intuition, at this point still unvalidated, that a document in a search
set is likely to be objective if its contents are repeated across the search set. This
section sketches two algorithms that naturally arise as attempts to exploit this
conjecture, and that could still be effective even if it turns out the subjective
documents resemble each other more than objective documents do.
Stopword removal
Before proceeding, it is important to note that common non-content-bearing terms
such as “the” do not represent the same thing as objective information, but could
potentially be treated the same as objective information if we simply look at frequency of occurrence. Therefore, stopword removal is indicated as an important
pre-processing step. Unfortunately, the commonly-used InQuery stopword list (Allan et al., 2000) contains terms like “less” that, while uninformative for topic-based
information retrieval, may be important for subjectivity detection. Therefore, we
used a 102-item list8 based solely on frequencies in the British National Corpus.
Clustering seems to be the obvious unsupervised, blank-slate approach to distinguishing classes when at least one class is believed to contain documents that are
more similar to documents in that class than to documents in the other class. Here,
we group the documents in a search set into two clusters, and then, given that the
data is (intentionally) skewed toward subjective documents as discussed above,
we label the smaller cluster as objective. We experimented with two commonly8
used clustering approaches. Single-link hierarchical clustering can create “stringy”
clusters, whereas centroid clustering, which successively merges the clusters whose
centroids are most similar, produces clusters that are more compact. Similarity
was measured via the cosine using 1 + log(tfi ) term weights, where tfi is the frequency of the ith term (inverse document frequency was not considered in order to
clarify comparison against the method outlined in the next subsection).
It is important to point out that we do envision situations in which clustering
might be ineffective. For example, the subjective documents might diverge so
much from each other that they cannot be forced into one cluster.9 We therefore
considered the following alternative instantiation of the intuition outlined in the
beginning of this section.
Idiosyncrasy-based approaches
Recall that the documents within a search set are on the same focused topic, since
they are simulating responses to a specific query. As mentioned in the introduction,
this setting raises the possibility that information repeated across most of the
documents in a search set is likely to be factual, since opinions probably exhibit
greater variability. This idea bears strong resemblance to the common informationretrieval practice of measuring inverse document frequency (idf) with respect to
the entire corpus in order to downweight common terms (Robertson, 2004; Sparck
Jones, 2004); but note that in our setting a factual item like a hotel’s address will
probably have low corpus idf but high search-set idf.
Specifically, suppose we have defined a search-set rarity function Raritys (t) that
varies inversely with the number of documents in the search set that contain the
An alternative would be to create three clusters, which might correspond to objective documents,
positive reviews, and negative reviews. However, in that case, how to make the subjective/objective
assignments is not clear.
term t. Then, we define the idiosyncrasy score of a document x as the average
search-set rarity of the terms it contains:
I(x) =
Raritys (t),
|vocab(x)| t∈x
where |vocab(x)| is the number of different terms (types) that occur in x. If
our intuitions are correct, then ranking by decreasing idiosyncrasy should put
subjective documents first and objective documents last.
Defining search-set rarity
There are various ways to define a search-set rarity function. The fundamental
quantity to consider is ns (t), the number of documents in the search set under
consideration that contain the term t. The possibilities we explored in this work
are: Raritys (t) = 1/ns (t), log(1/ns (t)), 1/ns (t)2 , or 1/ns (t); we refer to idiosyncrasy scoring functions based on these search-set rarity functions as idiosyncrasy,
idiosyncrasy (log), idiosyncrasy (square), and idiosyncrasy (sqrt), respectively.
The effect of document length
One potential issue with using Equation 6.1 is that there is the possibility that
factors other than what we discussed can come into play. In particular, as it
happens, in our dataset the reviews are on average much longer than the plot
summaries, so that classification based on length alone can actually be extremely
effective; however, this is not a characteristic that we can necessarily expect to
appear in other domains.10
Equation 6.1 normalizes the idiosyncrasy by a document’s vocabulary size,
which should compensate for document length. However, to further reduce the
On the other hand, we can expect that some domains will share this characteristic with our dataset.
For example, review-aggregation sites often place several reviews or review summaries on a single page.
influence that length might have with respect to our particular dataset, we formulated the following modified score function.11 Let lowest-rarity-vocabk (x) be the
k terms in x with the lowest search-set rarity (after stopword removal). Then, the
idiosyncracyk score of x is
Ik (x) =
Raritys (t).
t∈lowest-rarity-vocabk (x)
We are selecting the least rare terms because long documents seem more likely to
have terms with very high idiosyncrasy scores, and so could be selectively advantaged if it were the most rare terms that contributed to the ranking — again, our
goal here is to develop a function that is as length-neutral as possible.
Subjective documents form the “true” target class in the problem we are considering. However, evaluating with respect to the substantially smaller minority
(objective) class provides greater dynamic range for our empirical results, and so
these numbers are what we report.
An important evaluation issue is that the clustering methods we use assign
objective/subjective labels to documents, whereas the idiosyncrasy-based methods
give each document a score. To compare the two methods, we either have to
convert the clusterings into rankings, or delineate a decision policy by which to
We also looked at other ways to prevent length information from influencing the results, but all
seemed unsatisfactory. Concatenating objective documents together to lengthen them changes the distribution of terms across documents within the search set quite significantly — and this distribution
is, of course, the central quantity in an idiosyncrasy-based approach. Randomly removing words or
sentences from reviews to shorten them also alters the term distributions significantly, and moreover
could accidentally transform a review into an objective document. Shortening the reviews by applying
a sentence-level subjectivity classifier to discard the most objective sentences (Pang and Lee, 2004) can
preserve the subjectivity of the resultant extract, but still produces very different term distributions.
derive binary labels from the idiosyncrasy scores. We chose the former option,
given that our motivating scenario is that of a search engine presenting results
to a user, and search engines generally rank the documents they output. Specific
conversion policies are discussed below.
The development set (100 search sets) was used only for sanity-checking the
idiosyncrasy-based methods (including the value of the k parameter for idiosyncracyk ).
Rankings are evaluated by computing the precision of the top m documents, where
m is the number of objective documents in the search set. The results reported
are the average over the 1353 search sets in our test set.
We first consider the performance of some very simple baseline methods in order
to establish a reference point for our later comparisons.
Random-choice baseline: The precision that is expected to result if we simply
randomly ordered the documents is the percentage of objective documents within
the search set. The average objective-document percentage over the search sets is
POS baseline: Past research has found that the presence of adjectives within a
sentence is a strong indicator of that sentence being subjective (Hatzivassiloglou
and Wiebe, 2000; Wiebe et al., 2004). This suggests a simple non-“blank slate”
baseline (since some prior knowledge regarding subjectivity is utilized) of scoring a
document according to either (a) the percentage of subjective sentences within it,
assuming that a sentence containing adjectives is subjective (adj-sentence-percent),
or (b) the percentage of adjectives within the set of tokens the document contains
(adj-percent). The resulting precision using the Brill POS tagger (Brill, 1995)
Table 6.2: Average test-set results for objective-document precision at the number
of objective documents.
random-choice baseline
POS baseline (adj-sentence-percent)
POS baseline (adj-percent)
single-link (random ranking)
centroid (random ranking)
idiosyncrasy (square)
idiosyncrasy (sqrt)
idiosyncrasy (log)
idiosyncracyk (flipped)
single-link (idiosyncrasy (log) ranking) 81.7%
single-link (idiosyncracyk ranking)
centroid (idiosyncrasy (log) ranking)
centroid (idiosyncracyk ranking)
was 25.1% and 39.4%, respectively. Although there is a substantial performance
difference between the two, neither seems to be achieving high precision for the
minority class.
Comparison of individual algorithms
Clustering Recall that we need to convert clusterings into rankings. Clearly,
we should prefer all the documents in the smaller (presumably objective) cluster
Table 6.3: (Objective) precision and recall of the presumably objective cluster.
over all the documents in the larger cluster, so the question is simply how to
create within-cluster document orders. This is a non-trivial issue because of the
knowledge-poor nature of the setting we are considering.
To begin with, we consider a default policy in which documents within a cluster
are presented in random order. In this case, neither clustering method performs
particularly well. If we look at both the objective-document precision and recall of
the two methods with respect to the cluster deemed to be objective (as opposed to
precision at the number of objective documents, which is our evaluation measure),
we find that precision is generally higher, as shown in Table 6.3.
This indicates that a better method for within-cluster document ranking —
particularly for the subjective (larger) cluster, since the recall numbers show that
there are a large number of objective documents scattered through it — should
lead to performance improvements. We will discuss this possibility later on.
Idiosyncrasy Of the four idiosyncrasy methods that are based on the search-set
rarity of all the terms within a document to be scored, the log version performs the
best, and the squared version performs the worst. In fact, Table 6.2 seems to show
that concave-down functions of 1/ns (t) are preferable, and indeed, in information
retrieval, typically a log function is used to compress the differences in measured
inverse document frequency for extremely rare terms.
We now turn to the idiosyncracyk scoring function (Equation 6.2), which, recall, looks at the search-set rarity only of the k least idiosyncratic terms in the
document. (We used k = 20, which worked well for the development set.) Interestingly, this function, which we initially proposed simply as a corpus-dependent
“sanity check” that reduces a potential bias towards long documents (see Section
6.4.3), gives the best overall performance by a wide margin ... if we choose as most
objective the documents that have the highest idiosyncracyk .
Discussion The above result, at first blush, runs completely counter to our
intuitions that it should be the subjective documents that exhibit the most variation
and thus tend to contain less frequent terms. But upon reflection, there is a
reasonable basis for the phenomenon. While people’s opinions may (and do) differ,
what they may choose to discuss as the basis for their opinion may exhibit a high
degree of commonality; that is, there can be common agreement on what is worth
talking about, even if there is disagreement on what to think about it. As a concrete
example, take the movie The Unbearable Lightness of Being. The most common
words in the corresponding search set that appear in most of the reviews include
“surgeon”, “wife”, and “invasion” (which are all presumably non-subjective12 ).
For each of these words, there is only one objective document (out of three) that
contains it. (Words that are common to both the subjective and objective classes
include “Czechoslovakia” and “Sabina”.)
One could make the argument that what we are seeing is still an indirect
effect of the length bias in our corpus; the reasoning would be that the underlying
cause for reviews being longer is that authors of reviews tend to mention (the
It is possible that the term “surgeon” is being included in the reviews because it is suggestive of the
personality type of a character in the film, although to say that it is therefore evaluative with respect to
that character seems to be a bit of a stretch. A similar point could be made about the term “invasion”.
same) auxiliary details. But describing features of the topic under discussion is
a natural part of expressing a (useful) opinion, so it seems unfair to mandate
that a subjectivity-classification algorithm is not allowed to make use of these
descriptions. Also, as stated before, the idiosyncracyk algorithm was developed to
reduce the direct effects of length in comparison to the idiosyncrasy algorithms,
and the idiosyncracyk algorithm produced better results.
In any event, while different subjectivity-classification data might not exhibit
the same sort of characteristics as our corpus, we believe that looking at the distribution of terms within a stopword-filtered search set should still be a practical
and effective technique for separating subjective documents from objective ones.
Hybrid algorithms
As mentioned above, the precision of the clustering algorithms with random clusterinternal ranking, which definitively underperform the idiosyncrasy-based algorithms, ought to be improvable via the use of a better algorithm for ranking the
documents within each cluster. Fortunately, we have now learned that idiosyncrasy
is a good way to rank documents for subjectivity classification. This suggests a
family of algorithms that combine the two approaches we have outlined above:
first, apply one of the clustering techniques; then, order the documents within the
resultant clusters using one of our proposed functions incorporating term searchset rarity; finally, place the “objective” (smaller) cluster above the “subjective”
(larger) one.
The bottom “box” of Table 6.2 shows the results when the two best idiosyncrasybased scoring functions are integrated into the clustering methods. Interestingly,
when we use the logarithmic form for term search-set rarity, the combined algo-
rithms achieve clearly better performance (81.7% for single-link clustering, 83.7%
for centroid) than any of their single components (counting clustering with random ranking as a component). In the case of idiosyncracyk , the hybrid algorithm’s
performance represents a very large improvement over clustering alone, although
it slightly underperforms using idiosyncracyk in isolation. A final observation is
that for the hybrid methods, centroid clustering seems to be a better basis than
single-link clustering, even though the performance ordering is reversed when these
clustering algorithms are used in isolation. This can be partially explained by its
higher precision with respect to the “objective” cluster (see Table 6.3).
Conclusions and Future work
We have considered three “blank-slate” methods for document-level subjectivity
ranking of search sets, which models a scenario in which a search engine is being
used to find reviews on a specific item of interest. Our methods were motivated by
the intuition that objective documents should contain material that is frequently
repeated across the search set, since such information should correspond to facts.
It turned out, surprisingly, that in certain respects, subjective documents seem
more similar to each other than objective documents are (at least for the corpus
we experimented with). Yet, still, considering the distribution of terms across
documents in the search set leads to very good performance (precisions ranging
from the low 80’s to the 90’s) in this knowledge-impoverished setting.
Furthermore, we observed that after stopword removal, words with low searchset rarity may correspond to features of the matter at hand that people found
worth describing or commenting on. This may lead to algorithms to automatically
identify features of products in an unsupervised and knowledge-lean manner in the
context of review mining, or may be exploited to discover “hot” features that are
under heavy debate (or are simply being universally noted) for a given product.
We plan to develope a dataset that covers a range of representative queries with
webpages returned by actual search engines so that we can test our approaches in
a more realistic setting.
Chapter 7
Unanswered questions
The work presents a simple yet compelling musical allegory. A string
orchestra softly intones spacious chords representing the unfathomable
mystery of the universe. Against this background a trumpet poses the
eternal question: “Why do we exist?” Four flutes attempt to respond
but cannot agree among themselves. Their growing agitation finally
becomes intolerable, and they turn on the trumpet in a raucously mocking or berating fashion. In the end, the question remains unanswered,
and we are left only with the harmonies of the strings, impassive and
inscrutable as before.
– Paul Schiavo, program notes on Charles Ives’s The Unanswered Question
We have presented several sentiment analysis problems and discussed the new
challenges and new opportunities arising in this area. In particular, we discussed
several formulations that incorporate relationship information at different levels
of text corpus. The advantages of such formulations are not specific to sentiment
analysis. We have already seen that it is also effective in computer vision, com111
putational biology, and other fields. In natural language processing, Many categorization problems would benefit from a means for easily incorporating contextual
constraints, domain and linguistic knowledge, and class-membership probabilities
derived from corpus data. We are therefore optimistic that the techniques introduced here will prove beneficial in other areas of NLP as well.
Many questions remain unanswered. For instance, can we detect sarcasm? In
fact, this is one of the common questions we tend to receive: since there seems to
be a high correlation between the presence of sarcasm and negative sentiment, can
we use sarcasm detection to help polarity classification? The problem is it is not
clear that sarcasm detection is an easier task than polarity classification when our
only input is textual information. The following passage may help illustrate why:
“Am I busy?” exclaimed Authur. “Well, I’ve just got all these bulldozers and things to lie in front of because they’ll knock my house down if
I don’t, but other than that ... well, no, not especially, why?”
They don’t have sarcasm on Betelgeuse, and Ford Prefect often failed to
notice it unless he was concentrating. He said, “Good, is there anywhere
we can talk?”
— Douglas Adams, Hitchhiker’s Guide to the Galaxy
Note that even if they do have sarcasm on Betelgeuse, Ford Prefect also needs
certain world knowledge such as the consequences of having one’s house knocked
down in order to detect the presence of sarcasm here1 .
But even before we are capable of fully modeling such world knowledge computationally, there are many interesting questions the answers to which may help
Since the work described in this thesis addresses subjective language, it is perhaps acceptable to add
a personal, speculative remark that otherwise may have been inappropriate for an academic thesis: I
think we are still far from the point where it becomes critical to correctly model world knowledge.
us march towards the final steps. One that seems particularly interesting to me is
whether we can effectively model the transitions in sentiment (e.g., the “thwarted
expectations” narrative as discussed in Section 3.2.4) so that we can, for instance,
derive a more accurate assessment of the overall sentiment expressed. A sequential
document representation (such as presented in Lebanon (2006)) may be a good
candidate in this case as a step towards better modeling of the discourse structure. With more sophisticated demands for information, models that go beyond
the popular bag of words can become increasingly important; and as our examples
have shown, sentiment analysis problems can be particularly good testing grounds
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