A Product Feature Mining Method Based on Online Reviews
Product reviews contain a lot of useful information and have a significant influence on purchasing and selling behaviors.As the critical information from online reviews,product features have important theoretical and practical values.This paper presents a new product feature mining method,which improves the accuracy of candidate sets by extending user dictionary,and introduces synonyms for effectively pruning.In addition,a concept of sentiment index is proposed to select product features from candidate sets.This study respectively crawls online reviews of 4 products like cell phone and digital camera etc.For numerical experiment,and the result shows its feasibility and effectiveness.It not only well enhances the accuracy of existing research results,but also provides new research ideas for the product feature mining area.
review miningproduct featuresuser dictionarysynonymssentiment index
NETL
NSTL
万方数据
维普
