A Morpheme-based Method to Chinese Sentence-Level Sentiment Classification

Sentiment classification is a fundamental task in opinion mining. However, most existing systems require a sentiment lexicon to guide sentiment classification, which inevitably suffer from the problem of unknown words. In this paper, we present a morpheme-based fine-to-coarse strategy for Chinese sentence-level sentiment classification. To approach this, we first employ morphological productivity to extract sentiment morphemes from a sentiment dictionary and to calculate their polarity intensity at the same time. Then, we apply the acquired morpheme-level sentiment information to predict the semantic orientation of sentiment words and phrases within an opinionated sentence. Finally, we determine sentence-level semantic orientation by combining all the sentiment phrases and their relevant polarity scores. The experimental results on NTCIR-6 OAPT data set show our system can achieve state-of-the-art performance.