Supervised Approaches and Dependency Parsing for Chinese Opinion Analysis at NTCIR-8

In this paper, we describe our participating system, which is based on supervised approaches and dependency parsing, for opinion analysis on traditional Chinese texts at NTCIR-8. For opinionated sentence recognition, the supervised lexicon-based approach, SVM and Maximum Entropy are combined together. For polarity classification, we use only the supervised lexicon-based approach. For opinion holder and target identification, we, on the basis of dependency parsing, identify opinion holders by means of reporting verbs and identify opinion targets by considering both opinion holders and opinion-bearing words. The results show that among all the teams participating in the traditional Chinese task, our system achieve: 1) the highest F-measure on the opinionated sentence recognition task, 2) the second highest F-measure on the identification of both opinion holders and targets, 3) the middle ranking for opinion polarity classification.