A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts

A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts Bo Pang and Lillian Lee Department of Computer Science Cornell University Ithaca, NY 14853-7501 {pabo,llee}@cs.cornell.edu Abstract Sentiment analysis seeks to identify the view- point(s) underlying a text span; an example appli- cation is classifying a movie review as “thumbs up” or “thumbs down”. To determine this sentiment po- larity, we propose a novel machine-learning method that applies text-categorization techniques to just the subjective portions of the document. Extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints. 1 Introduction The computational treatment of opinion, sentiment, and subjectivity has recently attracted a great deal of attention (see references), in part because of its potential applications. For instance, information- extraction and question-answering systems could flag statements and queries regarding opinions rather than facts (Cardie et al., 2003). Also, it has proven useful for companies, recommender sys- tems, and editorial sites to create summaries of peo- ple’s experiences and opinions that consist of sub- jective expressions extracted from reviews (as is commonly done in movie ads) or even just a re- view’s polarity — positive (“thumbs up”) or neg- ative (“thumbs down”). Document polarity classification poses a signifi- cant challenge to data-driven methods, resisting tra- ditional text-categorization techniques (Pang, Lee, and Vaithyanathan, 2002). Previous approaches fo- cused on selecting indicative lexical features (e.g., the word “good”), classifying a document accord- ing to the number of such features that occur any- where within it. In contrast, we propose the follow- ing process: (1) label the sentences in the document as either subjective or objective, discarding the lat- ter; 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, al- though the sentence “The protagonist tries to pro- tect her good name” contains the word “good”, it tells us nothing about the author’s opinion and in fact could well be embedded in a negative movie review. Also, as mentioned above, subjectivity ex- tracts can be provided to users as a summary of the sentiment-oriented content of the document. Our results show that the subjectivity extracts we create accurately represent the sentiment in- formation of the originating documents in a much more compact form: depending on choice of down- stream polarity classifier, we can achieve highly sta- tistically significant improvement (from 82.8% to 86.4%) or maintain the same level of performance for the polarity classification task while retaining only 60% of the reviews’ words. Also, we ex- plore extraction methods based on a minimum cut formulation, which provides an efficient, intuitive, and effective means for integrating inter-sentence- level contextual information with traditional bag-of- words features. 2 Method 2.1 Architecture One can consider document-level polarity classi- fication 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 …

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