Domain dependent word polarity analysis for sentiment classification

The researches of sentiment analysis aim at exploring the emotional state of writers. The analysis highly depends on the application domains. Analyzing sentiments of the articles in different domains may have different results. In this study, we focus on corpora from three different domains in Traditional and Simplified Chinese, then examine the polarity degrees of vocabularies in these three domains, and propose methods to capture sentiment differences. Finally, we apply the results to sentiment classification with supervised SVM learning. The experiments show that the proposed methods can effectively improve the sentiment classification performance.