Constructing Chinese sentiment lexicon using bilingual information

Abstract

Currently, sentiment analysis has become a hot research topic in the natural language processing (NLP) field as it is highly valuable for many practical usages and theoretical studies. As a basic task in sentiment analysis, construction of sentiment lexicon aims to classify one word into positive, neutral or negative according to its sentiment orientation. However, when constructing a sentiment lexicon in Chinese, there are two major problems: 1) Chinese words are very ambiguous, which makes it hard to compute the sentiment orientation of a word; 2) Given the related research on sentiment analysis, available resource for constructing Chinese sentiment lexicons remains weak. Note that there are several corpus and lexicons in English sentiment analysis. In this study, we first use machine translation system with bilingual resources, i.e., English and Chinese information, then we get the sentiment orientation of Chinese words by computing the point-wise mutual information (PMI) values with English seed words. Experiment results from three domains demonstrate that the lexicon generated with our approach reaches an excellent precision and could cover domain information effectively. © 2013 Springer-Verlag.