Extracting Social Events Based on Timeline and Sentiment Analysis in Twitter Corpus

We propose a novel method for extracting social events based on timeline and sentiment analysis from social streams such as Twitter. When a big social issue or event occurs, it tends to dramatically increase in the number of tweets. Users write tweets to express their opinions. Our method uses these timeline and sentiment properties of social media streams to extract social events. On timelines term significance is calculated based on Chi-square measure. Evaluating the method on Korean tweet collection for 30 events, our method achieved 94.3% in average precision in the top 10 extracted events. The result indicates that our method is effective for social event extraction.