Research On Product Feature Extracion And Opinion Mining Of Online Reviews

With the rapid development of the WEB 2.0 technology,almost all types of network platform have the module of reviews,more and more consumers tend to deliver their opinions about the product at the network platform.Comments on these products are rich in valuable information,on the one hand,it can help consumers to make the right buying decision,but on the other hand,it can help enterprises to understand the consumers' real needs,gradually improve the quality of their products.Due to the large scale of online product comments,it is hard to read all the comments manually.Therefore,analyzing the online product reviews automatically,mining the opinion elements from the comment text,and showing the results in a clear and intuitive way have important research value.This thesis uses the fine-grained opinion mining technology,namely,based on product features to analysis online comments.The main content of this thesis is summarized as follows:1)Regarding problems of domain dependency and low recall,and according to analyze the lexical structure of a large number of comments corpora,this thesis proposes the adverbs based opinion elements extracting approach.First of all,realizing the iterative extraction of adverbs and opinion words based on the interdisciplinary seed adverbs,then realizing the iterative extraction of feature words and opinion words based on the opinion words.In addition,regarding the possible causes of noise in the process of extraction,this thesis uses three pruning methods to remove invalid feature words.2)Due to different users may use different words to express the same product features,so we need to merge the feature words that having the same meaning.Regarding both the semantic dictionary based similarity computing method and the literal based similarity computing method have their deficiencies,this thesis makes full use of existing dictionary resources,and the contextual constraint relations between feature words are mined to compute similarity,the two methods can complement each other in some degree and enhance the recall of recognizing the feature words that having the same meaning.3)Regarding the problem that the existing methods could not predict the orientation of some opinion words,this thesis proposes the clustering algorithm based predicting orientation approach.Through statistics of a large number of comments corpora,this thesis summarizes some factors that affecting the orientation of feature-opinion pairs,in order to make the rules to initialize the similarity between the assessment unit(features,opinion,number of negatives,modification adverbs).Then using the improved k-medoids algorithm to classify the assessment unit,so that the units that have the same orientation are in the same cluster,finally,predicting the orientation of the whole cluster according to the orientation of the seed opinion words.Finally,based on the work above,this thesis designs the online product reviews mining system.The system can mine the valuable information from the online product reviews,and show consumers' emotional orientation to each product feature.