A Comparative Study of Cross-Lingual Sentiment Classification

Abstract

The task of sentiment classification relies heavily on sentiment resources, including annotated lexicons and corpus. However, the sentiment resources in different languages are imbalanced. In particular, many reliable English resources are available on the Web, while reliable Chinese resources are scarce till now. Cross-lingual sentiment classification is a promising way for addressing the above problem by leveraging only English resources for Chinese sentiment classification. In this study, we conduct a comparative study to explore the challenges of cross-lingual sentiment classification. Different schemes for cross-lingual sentiment classification based on two dimensions have been compared empirically. Lastly, we propose to combine the different individual schemes into an ensemble. Experiment results demonstrate the effectiveness of the proposed method.