Emotion prediction of news articles from reader's perspective based on multi-label classification

Abstract

Most studies on emotion analysis and detection focus on the writer's perspective while emotion prediction is a kind emotion analysis from the reader's perspective. The existing emotion prediction techniques are primarily based on single label classification. Considering that many reader emotions are the combination of more than one base emotion, in this study, the reader emotion prediction is regarded as a multi-label classification problem. Various multi-label classification algorithms, problem transformation methods and various feature selection methods are investigated to classify the input documents into categories corresponding to different reader's emotions. The evaluations on a large-scale user-generated emotion corpus show that the random k-label sets classifier (RAkEL) with the feature selection based on the intersection of chi-square statistics and document frequency performs best.