Feature Subsumption for Opinion Analysis

Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), pages 440–448, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Feature Subsumption for Opinion Analysis Ellen Riloff and Siddharth Patwardhan School of Computing University of Utah Salt Lake City, UT 84112 {riloff,sidd}@cs.utah.edu Janyce Wiebe Department of Computer Science University of Pittsburgh Pittsburgh, PA 15260 wiebe@cs.pitt.edu Abstract Lexical features are key to many ap- proaches to sentiment analysis and opin- ion detection. A variety of representations have been used, including single words, multi-word Ngrams, phrases, and lexico- syntactic patterns. In this paper, we use a subsumption hierarchy to formally define different types of lexical features and their relationship to one another, both in terms of representational coverage and perfor- mance. We use the subsumption hierar- chy in two ways: (1) as an analytic tool to automatically identify complex features that outperform simpler features, and (2) to reduce a feature set by removing un- necessary features. We show that reduc- ing the feature set improves performance on three opinion classification tasks, espe- cially when combined with traditional fea- ture selection. 1 Introduction Sentiment analysis and opinion recognition are ac- tive research areas that have many potential ap- plications, including review mining, product rep- utation analysis, multi-document summarization, and multi-perspective question answering. Lexi- cal features are key to many approaches, and a va- riety of representations have been used, including single words, multi-word Ngrams, phrases, and lexico-syntactic patterns. It is common for dif- ferent features to overlap representationally. For example, the unigram “happy” will match all of the texts that the bigram “very happy” matches. Since both features represent a positive sentiment and the bigram matches fewer contexts than the unigram, it is probably sufficient just to have the unigram. However, there are many cases where a feature captures a subtlety or non-compositional meaning that a simpler feature does not. For exam- ple, “basket case” is a highly opinionated phrase, but the words “basket” and “case” individually are not. An open question in opinion analysis is how often more complex feature representations are needed, and which types of features are most valuable. Our first goal is to devise a method to automatically identify features that are represen- tationally subsumed by a simpler feature but that are better opinion indicators. These subjective ex- pressions could then be added to a subjectivity lex- icon (Esuli and Sebastiani, 2005), and used to gain understanding about which types of complex fea- tures capture meaningful expressions that are im- portant for opinion recognition. Many opinion classifiers are created by adopt- ing a “kitchen sink” approach that throws together a variety of features. But in many cases adding new types of features does not improve perfor- mance. For example, Pang et al. (2002) found that unigrams outperformed bigrams, and unigrams outperformed the combination of unigrams plus bigrams. Our second goal is to automatically iden- tify features that are unnecessary because similar features provide equal or better coverage and dis- criminatory value. Our hypothesis is that …

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