Cross-Lingual Sentiment Classification with Bilingual Document Representation Learning

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 1403–1412, Berlin, Germany, August 7-12, 2016. c⃝2016 Association for Computational Linguistics Cross-Lingual Sentiment Classification with Bilingual Document Representation Learning Xinjie Zhou, Xianjun Wan and Jianguo Xiao Institute of Computer Science and Technology, Peking University The MOE Key Laboratory of Computational Linguistics, Peking University {zhouxinjie,wanxiaojun,xiaojianguo}@pku.edu.cn Abstract Cross-lingual sentiment classification aims to adapt the sentiment resource in a resource-rich language to a resource-poor language. In this study, we propose a representation learning approach which simultaneously learns vector representa- tions for the texts in both the source and the target languages. Different from pre- vious research which only gets bilingual word embedding, our Bilingual Document Representation Learning model BiDRL directly learns document representations. Both semantic and sentiment correlations are utilized to map the bilingual texts into the same embedding space. The experiments are based on the multilingual multi-domain Amazon review dataset. We use English as the source language and use Japanese, German and French as the target languages. The experimental results show that BiDRL outperforms the state-of-the-art methods for all the target languages. 1 Introduction Sentiment analysis for online user-generated con- tents has become a hot research topic during the last decades. Among all the sentiment analysis tasks, polarity classification is the most widely s- tudied topic. It has been proved to be invaluable in many applications, such as opinion polling (Tang et al., 2012), customer feedback tracking (Gamon, 2004), election prediction (Tumasjan et al., 2010), stock market prediction (Bollen et al., 2011) and so on. Most of the current sentiment classification sys- tems are built on supervised machine learning algorithms which require manually labelled da- ta. However, sentiment resources are usually un- balanced in different languages. Cross-lingual sentiment classification aims to leverage the re- sources in a resource-rich language (such as En- glish) to classify the sentiment polarity of texts in a resource-poor language (such as Japanese). The biggest challenge for cross-lingual sentimen- t classification is the vocabulary gap between the source language and the target language. This problem is addressed with different strategies in different approaches. Wan (2009) use machine translation tools to translate the training data di- rectly into the target language. Meng et al. (2012) and Lu et al. (2011) exploit parallel unlabeled da- ta to bridge the language barrier. Prettenhofer and Stein (2010) use correspondence learning al- gorithm to learn a map between the source lan- guage and the target language. Recently, repre- sentation learning methods has been proposed to solve the cross-lingual classification problem (Xi- ao and Guo, 2013; Zhou et al., 2015). These meth- ods aim to learn common feature representations for different languages. However, most of the cur- rent researches only focus on bilingual word em- bedding. In addition, these models only use the se- mantic correlations between aligned words or sen- tences in different languages while the sentiment correlations are ignored. In this study, we propose a cross-lingual repre- sentation learning model BiDRL which simulta- neously learns both the word and …

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