Research On LDA Based Takeout User Reviews Mining And Sentiment Analysis

Thanks to the vigorous development of the Internet and the rise of e-commerce,the takeout platform has expanded rapidly,and more and more consumers order through takeout.Therefore,the comment data on takeout has become more and more huge,and behind this massive takeout comment contains great commercial value,which is of great significance to merchants,takeout platforms and even consumers themselves.This paper mainly analyzes the takeout comments of meituan platform from two aspects: emotion analysis and theme mining,so as to put forward improvement suggestions.The main research contents of this paper are as follows:(1)In this paper,the semantic association between emotion and negative emotion in the dictionary is analyzed on the basis of emotion expansion of vec-2so,which leads to the imperfect semantic association between emotion and negative emotion in the dictionary.The experimental results show that the accuracy of emotion classification has been improved after integrating Word2 Vec and SO-PMI,which is4.2% higher than that of general emotion dictionary,and 2.5% and 1% higher than Word2 Vec and SO-PMI respectively.It is verified that the domain emotion dictionary combined with SO-PMI and word2 vec has more effective emotion classification and recognition.(2)In order to improve the performance of emotion classification more effectively,this paper combines the optimized extended emotion dictionary with different emotion classification models.Experiments show that the classification performance of the combination of the two is significantly better than that of using emotion dictionary alone or using emotion classifiers such as SVM,naive Bayes and LSTM alone.In order to better generalization ability and higher emotion resolution,this paper also adjusts and optimizes the Laplace smoothing parameters of naive Bayes and the Gaussian kernel parameters of SVM.Through multiple comprehensive comparisons of experiments,it is concluded that the classification accuracy of the combination of fusion extended emotion dictionary and LSTM model is the highest and the classification performance is the best among these classification models.(3)Based on LDA theme model,this paper mines the positive and negative potential themes of catering takeout comments.The optimal number of topics under bipolar emotion is determined by calculating cosine similarity,and the deep-seated topic keywords of positive comments and negative comments are extracted respectively.Pyldavis is also used for visual presentation of LDA topics.Finally,according to the comprehensive analysis results,the corresponding reasonable suggestions are given to businesses and platforms...