Product Feature Extraction with a Combined Approach

Product review mining is the process of extracting opinions of customers in reviews which are expressed by natural language. As the first phrase of product review mining, product feature extraction decides the quality of subsequent phrases. In this paper, we build a combined approach based on bootstrapping and ID3, ID3 is used as a feature selection algorithm in the iteration of bootstrapping. Given the seed set and classification feature set, the combined approach can automatically extract textual patterns with different structures, and avoid the design of textual pattern structures and the design of similarity function among textual patterns. We implement an automated product feature extraction system with the combined approach. Compare to previous study, our system achieves higher precision and better portability.