Building word-emotion mapping dictionary for online news

Abstract

Sentiment analysis of online documents such as news articles, blogs and microblogs has received increasing attention. We propose an efficient method of automatically building the word-emotion mapping dictionary for social emotion detection. In the dictionary, each word is associated with the distribution on a series of human emotions. In addition, three different pruning strategies are proposed to refine the dictionary. Experiment on the real-world data sets has validated the effectiveness and reliability of the method. Compared with other lexicons, the dictionary generated using our approach is more adaptive for personalized data set, language-independent, fine-grained, and volume-unlimited. The generated dictionary has a wide range of applications, including predicting the emotional distribution of news articles and tracking the change of social emotions on certain events over time.