Research And Improvement On Model Of Opinion Mining

With the rapid development of E-commerce and social network,more and more netizens have expressed their opinions on various products,events and personalities,and the number of online commentary information has exploded.The collection and analysis of such information is important for individuals,organizations,societies and countries.For such a huge amount of data,human analysis is clearly unable to meet the demand,so the opinion mining for network information has become an important research topic,which has gradually been widely concerned about and in-depth study.The researches on opinion mining for network information mainly focus on aspect detection and sentiment analysis.In recent years,as an unsupervised machine learning model,LDA topic model has made great contributions in the field of opinion mining.However,there are some problems in the study of opinion mining: LDA-based models need to set the number of topics in advance,many models can not distinguish between aspect words and opinion words,many models can not identify aspect-specific opinion words,and rely too much on sentiment dictionaries for sentiment analysis.To solve these problems,this paper proposes a hybrid Hierarchical Dirichlet Process and Maximum Entropy-Latent Dirichlet Allocation(HDP-ME-LDA)model,which combines the advantages of Hierarchical Dirichlet Process-LDA(HDP-LDA)model and Maximum Entropy-LDA(MaxEnt-LDA)model.The HDP-ME-LDA model has the following contributions:(1)The model can automatically determine the number of topics without human intervention.There is more internal information of text using the number of topics which are generated by HDP automatic clustering than the number of topics which are decided by human's experience.(2)The model not only distinguishes aspect words from opinion words,but also distinguishes between global and local aspect words,global opinion words and aspect-specific local opinion words,making the granularity of opinion mining results more detailed,and the information presented is more specific and practical.(3)The model can not only extract topics and opinions,but also analyze the sentiment polarity of opinions.The model does not use the sentiment dictionaries.So it gets rid of dependence on sentiment dictionaries,and enhances the cross-domain of sentiment analysis.(4)The model uses clauses as the basic unit.Compared with other LDA-based model,it has retained the context information to the greatest extent.In this paper,HDP-ME-LDA model was implemented and the control experiment was performed on the appropriate datasets.The results of experiments show that HDP-ME-LDA results are more comprehensive and specific than JST,ASUM,MaxEnt-LDA and HDP-LDA.And this model has better performance in topic coherence,accuracy of word subjectivity classification,accuracy of recognition of local words and accuracy of sentiment classification.This model solves the problems raised in this paper.