Generating Focused Topic-Specific Sentiment Lexicons

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 585–594, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Generating Focused Topic-specific Sentiment Lexicons Valentin Jijkoun Maarten de Rijke Wouter Weerkamp ISLA, University of Amsterdam, The Netherlands jijkoun,derijke,w.weerkamp@uva.nl Abstract We present a method for automatically generating focused and accurate topic- specific subjectivity lexicons from a gen- eral purpose polarity lexicon that allow users to pin-point subjective on-topic in- formation in a set of relevant documents. We motivate the need for such lexicons in the field of media analysis, describe a bootstrapping method for generating a topic-specific lexicon from a general pur- pose polarity lexicon, and evaluate the quality of the generated lexicons both manually and using a TREC Blog track test set for opinionated blog post retrieval. Although the generated lexicons can be an order of magnitude more selective than the general purpose lexicon, they maintain, or even improve, the performance of an opin- ion retrieval system. 1 Introduction In the area of media analysis, one of the key tasks is collecting detailed information about opin- ions and attitudes toward specific topics from var- ious sources, both offline (traditional newspapers, archives) and online (news sites, blogs, forums). Specifically, media analysis concerns the follow- ing system task: given a topic and list of docu- ments (discussing the topic), find all instances of attitudes toward the topic (e.g., positive/negative sentiments, or, if the topic is an organization or person, support/criticism of this entity). For every such instance, one should identify the source of the sentiment, the polarity and, possibly, subtopics that this attitude relates to (e.g., specific targets of criticism or support). Subsequently, a (hu- man) media analyst must be able to aggregate the extracted information by source, polarity or subtopics, allowing him to build support/criticism networks etc. (Altheide, 1996). Recent advances in language technology, especially in sentiment analysis, promise to (partially) automate this task. Sentiment analysis is often considered in the context of the following two tasks: • sentiment extraction: given a set of textual documents, identify phrases, clauses, sen- tences or entire documents that express atti- tudes, and determine the polarity of these at- titudes (Kim and Hovy, 2004); and • sentiment retrieval: given a topic (and possi- bly, a list of documents relevant to the topic), identify documents that express attitudes to- ward this topic (Ounis et al., 2007). How can technology developed for sentiment analysis be applied to media analysis? In order to use a sentiment extraction system for a media analysis problem, a system would have to be able to determine which of the extracted sentiments are actually relevant, i.e., it would not only have to identify specific targets of all extracted sentiments, but also decide which of the targets are relevant for the topic at hand. This is a difficult task, as the relation between a topic (e.g., a movie) and specific targets of sentiments (e.g., acting or spe- cial effects in the movie) is not always straight- forward, in the …

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