Learning Multilingual Subjective Language via Cross-Lingual Projections

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 976–983, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics Learning Multilingual Subjective Language via Cross-Lingual Projections Rada Mihalcea and Carmen Banea Department of Computer Science University of North Texas rada@cs.unt.edu, carmenb@unt.edu Janyce Wiebe Department of Computer Science University of Pittsburgh wiebe@cs.pitt.edu Abstract This paper explores methods for generating subjectivity analysis resources in a new lan- guage by leveraging on the tools and re- sources available in English. Given a bridge between English and the selected target lan- guage (e.g., a bilingual dictionary or a par- allel corpus), the methods can be used to rapidly create tools for subjectivity analysis in the new language. 1 Introduction There is growing interest in the automatic extraction of opinions, emotions, and sentiments in text (sub- jectivity), to provide tools and support for various natural language processing applications. Most of the research to date has focused on English, which is mainly explained by the availability of resources for subjectivity analysis, such as lexicons and man- ually labeled corpora. In this paper, we investigate methods to auto- matically generate resources for subjectivity analy- sis for a new target language by leveraging on the resources and tools available for English, which in many cases took years of work to complete. Specif- ically, through experiments with cross-lingual pro- jection of subjectivity, we seek answers to the fol- lowing questions. First, can we derive a subjectivity lexicon for a new language using an existing English subjectivity lexicon and a bilingual dictionary? Second, can we derive subjectivity-annotated corpora in a new lan- guage using existing subjectivity analysis tools for English and a parallel corpus? Finally, third, can we build tools for subjectivity analysis for a new target language by relying on these automatically gener- ated resources? We focus our experiments on Romanian, selected as a representative of the large number of languages that have only limited text processing resources de- veloped to date. Note that, although we work with Romanian, the methods described are applicable to any other language, as in these experiments we (pur- posely) do not use any language-specific knowledge of the target language. Given a bridge between En- glish and the selected target language (e.g., a bilin- gual dictionary or a parallel corpus), the methods can be applied to other languages as well. After providing motivations, we present two ap- proaches to developing sentence-level subjectivity classifiers for a new target language. The first uses a subjectivity lexicon translated from an English one. The second uses an English subjectivity classifier and a parallel corpus to create target-language train- ing data for developing a statistical classifier. 2 Motivation Automatic subjectivity analysis methods have been used in a wide variety of text processing applica- tions, such as tracking sentiment timelines in on- line forums and news (Lloyd et al., 2005; Balog et al., 2006), review classification (Turney, 2002; Pang et al., 2002), mining opinions from product reviews (Hu and Liu, 2004), automatic expressive text-to-speech synthesis …

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