Generating a Non-English Subjectivity Lexicon: Relations That Matter

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 398–405, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics Generating a Non-English Subjectivity Lexicon: Relations That Matter Valentin Jijkoun and Katja Hofmann ISLA, University of Amsterdam Amsterdam, The Netherlands {jijkoun,k.hofmann}@uva.nl Abstract We describe a method for creating a non- English subjectivity lexicon based on an English lexicon, an online translation ser- vice and a general purpose thesaurus: Wordnet. We use a PageRank-like algo- rithm to bootstrap from the translation of the English lexicon and rank the words in the thesaurus by polarity using the net- work of lexical relations in Wordnet. We apply our method to the Dutch language. The best results are achieved when using synonymy and antonymy relations only, and ranking positive and negative words simultaneously. Our method achieves an accuracy of 0.82 at the top 3,000 negative words, and 0.62 at the top 3,000 positive words. 1 Introduction One of the key tasks in subjectivity analysis is the automatic detection of subjective (as opposed to objective, factual) statements in written doc- uments (Mihalcea and Liu, 2006). This task is essential for applications such as online market- ing research, where companies want to know what customers say about the companies, their prod- ucts, specific products’ features, and whether com- ments made are positive or negative. Another application is in political research, where pub- lic opinion could be assessed by analyzing user- generated online data (blogs, discussion forums, etc.). Most current methods for subjectivity identi- fication rely on subjectivity lexicons, which list words that are usually associated with positive or negative sentiments or opinions (i.e., words with polarity). Such a lexicon can be used, e.g., to clas- sify individual sentences or phrases as subjective or not, and as bearing positive or negative senti- ments (Pang et al., 2002; Kim and Hovy, 2004; Wilson et al., 2005a). For English, manually cre- ated subjectivity lexicons have been available for a while, but for many other languages such re- sources are still missing. We describe a language-independent method for automatically bootstrapping a subjectivity lex- icon, and apply and evaluate it for the Dutch lan- guage. The method starts with an English lexi- con of positive and negative words, automatically translated into the target language (Dutch in our case). A PageRank-like algorithm is applied to the Dutch wordnet in order to filter and expand the set of words obtained through translation. The Dutch lexicon is then created from the resulting ranking of the wordnet nodes. Our method has several ben- efits: • It is applicable to any language for which a wordnet and an automatic translation service or a machine-readable dictionary (from En- glish) are available. For example, the Eu- roWordnet project (Vossen, 1998), e.g., pro- vides wordnets for 7 languages, and free on- line translation services such as the one we have used in this paper are available for many other languages as well. • The method ranks all (or almost all) entries of …

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