Multi-label Emotion Classification for Tweets in Weibo: Method and Application

The booming development of Online Social Networks (OSNs) provides a novel way of expressing emotions. Research on emotion analysis for tweets in OSNs is becoming increasingly popular in recent years. Traditional emotion analysis only classifies one tweet into a single emotion category. However, in reality, a tweet may belong to several different emotion categories. In the paper, our goal is to predict all emotion labels of each tweet. We use graphic emoticons, punctuation expressions together with a tiny but accurate lexicon to label data and provide a Multi-label Emotion Classification algorithm (MEC) for tweets in Weibo (so called Chinese Twitter). Our method has superior performance to the state-of-the-art method under both single-label and multi-label evaluation measures. We also carried out a case study on Weibo dataset of Malaysia Missing Flight. We came to several meaningful conclusions such as "The outbreak of Anger has a delay after breaking point of Sadness".