A novel context-based implicit feature extracting method
Li Sun, Sheng Li, Jiyun Li, JuTao Lv · 2014
One of the major steps for opinion mining is to extract product features. The vast majority of existing approaches focus on explicit feature identification, few attempts have been made to identify implicit features in reviews, however; people tend to express their opinions with simple structures and brachylogies, which lead to more implicit features in reviews. By analyzing the characteristics of product reviews in Chinese on the Internet, this paper proposes a novel context-based implicit feature extracting method. We extract the implicit features according to the opinion words and the similarity between the product features in the implicit features' context. We also build a matrix to show the relationship between opinion words and product features, then use a new algorithm to filter the noises in the matrix. Experiments show that our method provides higher accuracy in extracting the implicit features.