Sentiment Classification in Turn-Level Interactive Chinese Texts of E-learning Applications

To solve the problem of emotional illiteracy in current e-Learning environment, researches on sentiment analysis now get more attentions. This paper focuses on recognizing emotion from interactive Chinese texts (ICTs). Through observation, firstly, characteristics of ICTs are discussed. Then two kinds of feature sets, frequency based feature set and interaction related feature set, are presented. Finally, the corresponding feature extraction and selection for ICTs are presented. To validate the feature sets and choose the best method of sentiment analysis, we carry out a number of experiments. The experiments' results show that, combining with syntax based feature set, frequency based feature set and interaction related feature set can improve algorithm classification performance, and multi-class classifier and the tree based methods perform better than others.