Tracing Trending Topics by Analyzing the Sentiment Status of Tweets

Dongjin Choi, Myunggwon Hwang, Jeongin Kim, Byeongkyu Ko, Pankoo Kim

Information spreads much faster through social networking services (SNSs) than through traditional news media because users can upload data anytime, anywhere. SNSs users are likely to express their emotional status to let their friends or other users know how they feel about certain events. This is the main reason why many studies have employed social media data to uncover hidden facts or issues by analyzing social relationships and reciprocated messages between users. The main goal of this study is to discover who is isolated, why, and how the issue of social bullying can be addressed through an in-depth analysis of negative Tweets. For this, our study takes the basic approach by tracking events considered to be exciting by users and then analyzing the sentiment status of their Tweets collected between November and December 2009 by Stanford University. The results suggest that users tend to be happier during evenings than during afternoons. The results also identify the precise date of breaking news.