Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 347–354, Vancouver, October 2005. c⃝2005 Association for Computational Linguistics Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis Theresa Wilson Intelligent Systems Program University of Pittsburgh Pittsburgh, PA 15260 twilson@cs.pitt.edu Janyce Wiebe Department of Computer Science University of Pittsburgh Pittsburgh, PA 15260 wiebe@cs.pitt.edu Paul Hoffmann Intelligent Systems Program University of Pittsburgh Pittsburgh, PA 15260 hoffmanp@cs.pitt.edu Abstract This paper presents a new approach to phrase-level sentiment analysis that first determines whether an expression is neu- tral or polar and then disambiguates the polarity of the polar expressions. With this approach, the system is able to automat- ically identify the contextual polarity for a large subset of sentiment expressions, achieving results that are significantly bet- ter than baseline. 1 Introduction Sentiment analysis is the task of identifying positive and negative opinions, emotions, and evaluations. Most work on sentiment analysis has been done at the document level, for example distinguishing pos- itive from negative reviews. However, tasks such as multi-perspective question answering and sum- marization, opinion-oriented information extraction, and mining product reviews require sentence-level or even phrase-level sentiment analysis. For exam- ple, if a question answering system is to successfully answer questions about people’s opinions, it must be able to pinpoint expressions of positive and negative sentiments, such as we find in the sentences below: (1) African observers generally approved+ of his victory while Western governments denounced− it. (2) A succession of officers filled the TV screen to say they supported+ the people and that the killings were “not tolerable−.” (3) “We don′t hate+ the sinner,” he says, “but we hate− the sin.” A typical approach to sentiment analysis is to start with a lexicon of positive and negative words and phrases. In these lexicons, entries are tagged with their a priori prior polarity: out of context, does the word seem to evoke something positive or some- thing negative. For example, beautiful has a positive prior polarity, and horrid has a negative prior polar- ity. However, the contextual polarity of the phrase in which a word appears may be different from the word’s prior polarity. Consider the underlined polar- ity words in the sentence below: (4) Philip Clapp, president of the National Environ- ment Trust, sums up well the general thrust of the reaction of environmental movements: “There is no reason at all to believe that the polluters are sud- denly going to become reasonable.” Of these words, “Trust,” “well,” “reason,” and “rea- sonable” have positive prior polarity, but they are not all being used to express positive sentiments. The word “reason” is negated, making the contex- tual polarity negative. The phrase “no reason at all to believe” changes the polarity of the proposition that follows; because “reasonable” falls within this proposition, its contextual polarity becomes nega- tive. The word “Trust” is simply part of a referring expression and is not being used to express a senti- ment; thus, its contextual polarity is neutral. Simi- larly for “polluters”: …

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