Coarse-Fine Opinion Mining - WIA in NTCIR-7 MOAT Task

This paper presents an opinion analysis system developed by CUHK_PolyU_Tsinghua Web Information Analysis Group (WIA), namely WIA-Opinmine, for NTCIR-7 MOAT Task. Different from most existing opinion mining systems, which recognize opinionated sentences as one-step classification procedure, WIAOpinmine adopts a multi-pass coarse-fine analysis strategy. A base classifier firstly coarsely estimates the opinion of sentences and the document. The obtained document-level and sentence-level opinions are then incorporated in a complex classifier to re-analyze the opinion of sentences to obtain refined sentence and document opinions. The updated opinion features are feed back to the complex classifier to further refine the opinion analysis. Such circles terminate until the analysis results converge. Similar strategy is adopted in sentence-topic relevance estimation. Furthermore, the mutual reinforcement between the analysis of sentence relevance and sentence opinion are integrated in one framework in WIA-Opinmine. Evaluations on NTCIR-7 MOAT Traditional Chinese and Simplified Chinese sides show that WIA-Opinmine achieves the best precisions performance in five subtasks and the best F performance in three subtasks including polarity determination, opinion holder recognition and opinion target recognition. This results show that the proposed framework integrating coarse-fine opinion mining strategy and the mutual reinforcement between the analysis of sentence relevance and sentence opinion is promising.