A Chinese Product Feature Extraction Method Based on KNN Algorithm

The product feature set of online reviews obtained by the current product feature extraction methods has a low coverage rate of review information. In order to solve this problem, this paper proposes a method of product feature extraction based on KNN algorithm. We establish the classification system of product feature set firstly. Then we extract part of product features as training set manually, and according to similarity between words and the classification system, the product features of all reviews are quickly classified and extracted. At last, the PMI algorithm is used to filter and supplement it to improve the correct rate and the review information coverage rate of product feature set. Through the examples of online clothing reviews data in the Taobao platform, we prove that this method can effectively improve the review information coverage rate of product feature set.