Sentiment-based and hashtag-based Chinese online bursty event detection

How to detect bursty events in data streams on social media is a hot research topic in natural language processing. However, current methods for extracting bursty events suffer from poor accuracy and low efficiency. Fortunately, sentiment analysis has been applied to event detection, which has improved the performance greatly. Inspired by this, this paper proposes a new model which utilizes sentiment analysis for Chinese bursty event detection. First, we build a sentiment co-occurrence graph offline and apply it to analyze microblog sentiment. Plutchik’s emotion wheel is the base for the sentiment classification of the graph. Second, sentiment is used as features to detect bursts in microblog streams online. At last, we exploit regular expressions to extract hashtags in bursty periods and segment hashtags into keywords. By using mutual information and frequent patterns, we fetch words relevant to hashtags as keywords to form events. This approach can detect bursty events online while analyzing the sentiment of microblogs. The experimental results on a large real dataset show that our method can detect bursty events with higher accuracy in a shorter time than traditional methods.