Product Review Mining Based On Features Set

Along with the fast development of E-commerce and the population of Web 2.0, more and more consumers go shopping online and post reviews of products .Those customer reviews are excellent sources for potential customers to gain more information of products, and may have some impacts on potential consumers' behavior. To those unstructured and scattered opinions, product opinion mining is being developed to exploit these sources to help companies and individuals to gain such information effectively and easily, using NLP techniques automated.We study the problem of opinion mining at the feature-based level. After analyzing the limitation of the existing methods of product feature identification, we propose a method based on product features system to better mining and summarizing customer reviews. Firstly, we manually come up product features from user guide of product and a small amount of product reviews text, and establish a product features system of the product category, according to the theory of establishing product features system. Then, we identify new product features in the additional opinions by using pointwise Mutual Information (PMI), in order to improve product features system dynamically. Secondly, we utilize these positive and negative words in HowNet as the opinion seed list. And we expand the seed list by utilizing the adjective synonym set and antonym set in WordNet to predict the semantic orientations of adjectives. Thirdly, according to the number of feature words, opinion words and negative words in opinion sentence, we calculate product features'sentiment score by utilizing conjunctions and the principle of proximity. Then, we aggregate scores of each level features from the lowest layer to the upper layer by using the hierarchy of product features system. Finally, we extract product reviews of Canon Power shot SD780 IS on www.Amazon.com as a sample, analysis and obtain the opinion mining result of this camera based on previous theory research. And the overall result and partial result are showed by using the product features system and product indicators of evaluation respectively.