Personality based public sentiment classification in microblog

Abstract

In recent years, microblog has become one of the most widely used social media for people to exchange ideas and express emotions. As information propagates fast in social network, it's crucial for governments and public agencies to effectively monitor public sentiment implied in user-generated content. Most previous work of public sentiment analysis takes tweets of different users as a whole without considering the diverse word use of people. Thus, some sentiment words may be neglected in the process of analysis because they are only used by people of specific groups. Inspired by previous psychological findings that personality influences the ways people write and talk, we propose a personality based sentiment classification method. In order to capture more useful but not widely used sentiment words, our approach extracts textual features for people of different personality traits based on the Big Five model. Moreover, we adopt an ensemble learning strategy to utilize both personality related and commonly used textual features. Experimental study shows the effectiveness of our method.