Chinese Sentence-Level Sentiment Classification Based on Fuzzy Sets

This paper presents a fuzzy set theory based approach to Chinese sentence-level sentiment classification. Compared with traditional topic-based text classification techniques, the fuzzy set theory provides a straightforward way to model the intrinsic fuzziness between sentiment polarity classes. To approach fuzzy sentiment classification, we first propose a fine-to-coarse strategy to estimate sentence sentiment intensity. Then, we define three fuzzy sets to represent the respective sentiment polarity classes, namely positive, negative and neutral sentiments. Based on sentence sentiment intensities, we further build membership functions to indicate the degrees of an opinionated sentence in different fuzzy sets. Finally, we determine sentence-level polarity under maximum membership principle. We show that our approach can achieve promising performance on the test set for Chinese opinion analysis pilot task at NTCIR-6.