Mining Sentiment Words from Microblogs for Predicting Writer-Reader Emotion Transition

The conversations between posters and repliers in microblogs form a valuable writer-reader emotion corpus. In a microblog conversation, the writer of the initial post and the reader who replies to the initial post can both express their emotions. The process of changing from writer emotion to reader emotion is called a writer-reader emotion transition in this paper. Log relative frequency ratio is adopted to investigate the linguistic features that affect emotion transitions, and the results are used to predict writers' and readers' emotions. A 4-class emotion transition predictor, a 2-class writer emotion predictor, and a 2-class reader emotion predictor are proposed and compared.