Exploiting Lexical Sentiment Membership-Based Features to Polarity Classification
Jiayin Song · Beijing Daxue Xuebao. Zirankexueban · 2016
A lexical sentiment membership based feature representation was presented for Chinese polarity classification under the framework of fuzzy set theory. TF-IDF weighted words are used to construct the corresponding positive and negative polarity membership for each feature word, and the log-ratio of each membership is computed. A support vector machines based polarity classifier is built with the membership logratios as its features. Furthermore, the classifier is evaluated over different datasets, including a corpus of reviews on automobile products, the NLPCC2014 data for sentiment classification evaluation and the IMDB film comments. The experimental results show that the proposed sentiment membership feature representation outperforms the state of the art feature representations such as the Boolean features, the frequent-based features and the word embeddings based features.