Supervised Approaches and Ensemble Techniques for Chinese Opinion Analysis at NTCIR-7

For the opinion analysis task on traditional Chinese texts at NTCIR-7, supervised approaches and ensemble techniques have been used and compared in our participating system. Two kinds of supervised approaches were employed here: 1) the supervised lexicon-based approach, and 2) machine learning approaches. Ensemble techniques were also used to combine the results given by different approaches. By making use of these approaches and ensemble methods in various combinations, we submitted three runs for each of the two subtasks we participated in: opinionated sentence recognition and opinion polarity classification. The results show that our system achieved state-of-the-art performance on both subtasks: the highest F-measure on the opinionated sentence recognition task and the second highest F-measure on the opinion polarity classification task amongst all runs submitted by seven participants. Furthermore, the ensemble combination of different classifiers markedly outperformed individual classifiers on the opinion polarity classification task, without showing much improvement, however, on the opinionated sentence recognition task.