A Study of Information Retrieval Weighting Schemes for Sentiment Analysis

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1386–1395, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics A study of Information Retrieval weighting schemes for sentiment analysis Georgios Paltoglou University of Wolverhampton Wolverhampton, United Kingdom g.paltoglou@wlv.ac.uk Mike Thelwall University of Wolverhampton Wolverhampton, United Kingdom m.thelwall@wlv.ac.uk Abstract Most sentiment analysis approaches use as baseline a support vector machines (SVM) classifier with binary unigram weights. In this paper, we explore whether more sophisticated feature weighting schemes from Information Retrieval can enhance classification accuracy. We show that vari- ants of the classic tf.idf scheme adapted to sentiment analysis provide significant increases in accuracy, especially when us- ing a sublinear function for term frequency weights and document frequency smooth- ing. The techniques are tested on a wide selection of data sets and produce the best accuracy to our knowledge. 1 Introduction The increase of user-generated content on the web in the form of reviews, blogs, social networks, tweets, fora, etc. has resulted in an environ- ment where everyone can publicly express their opinion about events, products or people. This wealth of information is potentially of vital im- portance to institutions and companies, providing them with ways to research their consumers, man- age their reputations and identify new opportuni- ties. Wright (2009) claims that “for many busi- nesses, online opinion has turned into a kind of virtual currency that can make or break a product in the marketplace”. Sentiment analysis, also known as opinion min- ing, provides mechanisms and techniques through which this vast amount of information can be pro- cessed and harnessed. Research in the field has mainly, but not exclusively, focused in two sub- problems: detecting whether a segment of text, ei- ther a whole document or a sentence, is subjective or objective, i.e. contains an expression of opin- ion, and detecting the overall polarity of the text, i.e. positive or negative. Most of the work in sentiment analysis has fo- cused on supervised learning techniques (Sebas- tiani, 2002), although there are some notable ex- ceptions (Turney, 2002; Lin and He, 2009). Pre- vious research has shown that in general the per- formance of the former tend to be superior to that of the latter (Mullen and Collier, 2004; Lin and He, 2009). One of the main issues for supervised approaches has been the representation of docu- ments. Usually a bag of words representation is adopted, according to which a document is mod- eled as an unordered collection of the words that it contains. Early research by Pang et al. (2002) in sentiment analysis showed that a binary unigram- based representation of documents, according to which a document is modeled only by the pres- ence or absence of words, provides the best base- line classification accuracy in sentiment analysis in comparison to other more intricate representa- tions using bigrams, adjectives, etc. Later research has focused on extending the document representation with more complex fea- tures such as structural or syntactic informa- tion (Wilson et al., 2005), …

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