An Analysis of Radicals-based Features in Subjectivity Classification on Simplified Chinese Sentences

Chinese radicals are linguistic elements smaller than Chinese characters1. Normally, a radical is a semantic category and almost all characters contain radicals or are radicals themselves. In subjectivity classification on sentences, we can use radicals to represent characters, which reduce the scale of word space while keep the subjectivity information. In this paper, we manually labeled a character set to build a high-quality radical-character mapping, and then the mapping is used to generalize character-based features with radicals. In experiments, we at first evaluated the performance when directly generalizing characters with radicals, and then offer a hypothesis that can reduce noises. Experiments show that this approach based on our hypothesis can reduce feature space while keep or improve the performance, which is especially useful when the training samples are scarce. keyword: sentiment analysis, subjectivity classification, radical, Chinese character