Joint Bilingual Sentiment Classification with Unlabeled Parallel Corpora

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 320–330, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Joint Bilingual Sentiment Classification with Unlabeled Parallel Corpora Bin Lu1,3*, Chenhao Tan2, Claire Cardie2 and Benjamin K. Tsou3,1 1 Department of Chinese, Translation and Linguistics, City University of Hong Kong, Hong Kong 2 Department of Computer Science, Cornell University, Ithaca, NY, USA 3 Research Centre on Linguistics and Language Information Sciences, Hong Kong Institute of Education, Hong Kong lubin2010@gmail.com, {chenhao, cardie}@cs.cornell.edu, btsou99@gmail.com Abstract Most previous work on multilingual sentiment analysis has focused on methods to adapt sentiment resources from resource-rich languages to resource-poor languages. We present a novel approach for joint bilingual sentiment classification at the sentence level that augments available labeled data in each language with unlabeled parallel data. We rely on the intuition that the sentiment labels for parallel sentences should be similar and present a model that jointly learns improved mono- lingual sentiment classifiers for each language. Experiments on multiple data sets show that the proposed approach (1) outperforms the mono- lingual baselines, significantly improving the accuracy for both languages by 3.44%-8.12%; (2) outperforms two standard approaches for leveraging unlabeled data; and (3) produces (albeit smaller) performance gains when employing pseudo-parallel data from machine translation engines. 1 Introduction The field of sentiment analysis has quickly attracted the attention of researchers and practitioners alike (e.g. Pang et al., 2002; Turney, 2002; Hu and Liu, 2004; Wiebe et al., 2005; Breck et al., 2007; Pang and Lee, 2008). 1 Indeed, sentiment analysis systems, which mine opinions from textual sources (e.g. news, blogs, and reviews), can be used in a wide variety of *The work was conducted when the first author was visiting Cornell University. applications, including interpreting product reviews, opinion retrieval and political polling. Not surprisingly, most methods for sentiment classification are supervised learning techniques, which require training data annotated with the appropriate sentiment labels (e.g. document-level or sentence-level positive vs. negative polarity). This data is difficult and costly to obtain, and must be acquired separately for each language under consideration. Previous work in multilingual sentiment analysis has therefore focused on methods to adapt sentiment resources (e.g. lexicons) from resource- rich languages (typically English) to other languages, with the goal of transferring sentiment or subjectivity analysis capabilities from English to other languages (e.g. Mihalcea et al. (2007); Banea et al. (2008; 2010); Wan (2008; 2009); Prettenhofer and Stein (2010)). In recent years, however, sentiment-labeled data is gradually becoming available for languages other than English (e.g. Seki et al. (2007; 2008); Nakagawa et al. (2010); Schulz et al. (2010)). In addition, there is still much room for improvement in existing monolingual (including English) sentiment classifiers, especially at the sentence level (Pang and Lee, 2008). This paper tackles the task of bilingual sentiment analysis. In contrast to previous work, we (1) assume that some amount of sentiment- labeled data is available for the language pair under study, and (2) investigate methods to simultaneously improve sentiment classification for …

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