Semi-Supervised Polarity Lexicon Induction

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 675–682, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics Semi-Supervised Polarity Lexicon Induction Delip Rao∗ Department of Computer Science Johns Hopkins University Baltimore, MD delip@cs.jhu.edu Deepak Ravichandran Google Inc. 1600 Amphitheatre Parkway Mountain View, CA deepakr@google.com Abstract We present an extensive study on the prob- lem of detecting polarity of words. We consider the polarity of a word to be ei- ther positive or negative. For example, words such as good, beautiful, and won- derful are considered as positive words; whereas words such as bad, ugly, and sad are considered negative words. We treat polarity detection as a semi-supervised la- bel propagation problem in a graph. In the graph, each node represents a word whose polarity is to be determined. Each weighted edge encodes a relation that ex- ists between two words. Each node (word) can have two labels: positive or negative. We study this framework in two differ- ent resource availability scenarios using WordNet and OpenOffice thesaurus when WordNet is not available. We report our results on three different languages: En- glish, French, and Hindi. Our results in- dicate that label propagation improves sig- nificantly over the baseline and other semi- supervised learning methods like Mincuts and Randomized Mincuts for this task. 1 Introduction Opinionated texts are characterized by words or phrases that communicate positive or negative sen- timent. Consider the following example of two movie reviews1 shown in Figure 1. The posi- tive review is peppered with words such as enjoy- able, likeable, decent, breathtakingly and the negative ∗Work done as a summer intern at Google Inc. 1Source: Live Free or Die Hard, rottentomatoes.com Figure 1: Movie Reviews with positive (left) and negative (right) sentiment. comment uses words like ear-shattering, humorless, unbearable. These terms and prior knowledge of their polarity could be used as features in a su- pervised classification framework to determine the sentiment of the opinionated text (E.g., (Esuli and Sebastiani, 2006)). Thus lexicons indicating po- larity of such words are indispensable resources not only in automatic sentiment analysis but also in other natural language understanding tasks like textual entailment. This motivation was seen in the General Enquirer effort by Stone et al. (1966) and several others who manually construct such lexicons for the English language.2 While it is possible to manually build these resources for a language, the ensuing effort is onerous. This mo- tivates the need for automatic language-agnostic methods for building sentiment lexicons. The im- portance of this problem has warranted several ef- forts in the past, some of which will be reviewed here. We demonstrate the application of graph-based semi-supervised learning for induction of polar- ity lexicons. We try several graph-based semi- 2The General Inquirer tries to classify English words along several dimensions, including polarity. 675 supervised learning methods like Mincuts, Ran- domized Mincuts, and Label Propagation. In par- ticular, we define a graph with nodes consisting of the words or phrases to be …

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