Corse-fine opinion mining

Most existing opinion mining systems recognize opinionated sentences and determine their polarity as one-step classification procedure. This paper proposes a different multi-pass coarse-fine opinion mining framework. In this framework, a base classifier firstly coarsely estimates the opinion of sentences. The obtained sentence-, paragraph- and document-level opinions are incorporated in an improved classifier as features to re-estimate the opinion of sentences. The updated opinions are feed back to the classifier for further refining the sentence opinion until the classifier outputs converge. Three base classifiers are incorporated in this coarse-fine opinion mining framework, respectively. Their performances are evaluated on NTCIR-6 and NTCIR-7 opinion analysis dataset. The achieved performance improvements show that the proposed coarse-fine strategy is effective to improve the developed opinion mining classifiers.