Emotion Classification of Online News Articles from the Reader's Perspective

Past studies on emotion classification focus on the writer’s emotional state. This research addresses the reader aspect instead. The classification of documents into reader-emotion categories has several applications. One of them is to integrate reader-emotion classification into a web search engine to allow users to retrieve documents that contain relevant contents and at the same time instill proper emotions. In this paper, we automatically classify documents into reader-emotion categories, and examine classification performance under different feature settings. Experiments show that certain feature combinations achieve good accuracy. We also compare the best classifier’s classification results with the emotional distributions of documents to determine how closely the classifier models the underlying reader behavior. Finally, we investigate the feasibility of emotion ranking.