Co-Training for Cross-Lingual Sentiment Classification

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 235–243, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Co-Training for Cross-Lingual Sentiment Classification Xiaojun Wan Institute of Compute Science and Technology & Key Laboratory of Computational Lin- guistics, MOE Peking University, Beijing 100871, China wanxiaojun@icst.pku.edu.cn Abstract The lack of Chinese sentiment corpora limits the research progress on Chinese sentiment classification. However, there are many freely available English sentiment corpora on the Web. This paper focuses on the problem of cross-lingual sentiment classification, which leverages an available English corpus for Chi- nese sentiment classification by using the Eng- lish corpus as training data. Machine transla- tion services are used for eliminating the lan- guage gap between the training set and test set, and English features and Chinese features are considered as two independent views of the classification problem. We propose a co- training approach to making use of unlabeled Chinese data. Experimental results show the effectiveness of the proposed approach, which can outperform the standard inductive classifi- ers and the transductive classifiers. 1 Introduction Sentiment classification is the task of identifying the sentiment polarity of a given text. The senti- ment polarity is usually positive or negative and the text genre is usually product review. In recent years, sentiment classification has drawn much attention in the NLP field and it has many useful applications, such as opinion mining and summa- rization (Liu et al., 2005; Ku et al., 2006; Titov and McDonald, 2008). To date, a variety of corpus-based methods have been developed for sentiment classification. The methods usually rely heavily on an anno- tated corpus for training the sentiment classifier. The sentiment corpora are considered as the most valuable resources for the sentiment classifica- tion task. However, such resources in different languages are very imbalanced. Because most previous work focuses on English sentiment classification, many annotated corpora for Eng- lish sentiment classification are freely available on the Web. However, the annotated corpora for Chinese sentiment classification are scarce and it is not a trivial task to manually label reliable Chinese sentiment corpora. The challenge before us is how to leverage rich English corpora for Chinese sentiment classification. In this study, we focus on the problem of cross-lingual senti- ment classification, which leverages only English training data for supervised sentiment classifica- tion of Chinese product reviews, without using any Chinese resources. Note that the above prob- lem is not only defined for Chinese sentiment classification, but also for various sentiment analysis tasks in other different languages. Though pilot studies have been performed to make use of English corpora for subjectivity classification in other languages (Mihalcea et al., 2007; Banea et al., 2008), the methods are very straightforward by directly employing an induc- tive classifier (e.g. SVM, NB), and the classifica- tion performance is far from satisfactory because of the language gap between the original lan- guage and the translated language. In this study, we propose a co-training ap- proach to improving the …

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