A Bootstrapping Based Refinement Framework for Mining Opinion Words and Targets
Qiyun Zhao, Hao Wang, Pin Lv, Chen Zhang · 2014
This paper proposes a novel bootstrapping based framework jointed with automatic refinement to extract opinion words and targets. We employ a reasonable set of opinion seed words and pre-defined rules to start bootstrapping. We leverage statistical word co-occurrence and dependency patterns for propagation between opinion words and targets. A Sentiment Graph Model (SGM) is constructed to evaluate these opinion relations. Furthermore, we employ Automatic Rule Refinement (ARR) to refine the rules to extract false results. By using false results pruning and ARR process, we can efficiently alleviate the error propagation problem in traditional bootstrapping-based methods. Preliminary evaluation shows the effectiveness of our method.