Analysis Of Public Opinion On Weibo Topic "Advanced On-demand" Based On LSTM

Social networking sites reflect the life rules and behaviors of people’s communication and interaction.Up to now,the Internet has produced a large number of subjective texts released by users,making text-study social networks of great value to the development of these industries in the Internet era.How to mine the emotional information of netizens from subjective texts on the Internet has become a hot research topic in natural language processing and research institutions.Therefore,this article uses a combination of deep learning classification methods and public opinion development trend analysis to analyze public opinion on "advanced ondemand" microblog topic data,and dig out consumers’ attitudes and opinions on such issues.This article uses a web crawler to crawl Weibo topic "advanced ondemand" comments,blog posts,and various variable information of the author of the text publishing as the original data of this article.A classification model is constructed through natural language processing technology to analyze sentimentality: 1)Text data prediction Processing--Deduplication,missing value processing,regularization processing,word segmentation,removal of stop words;2)Word vector construction--Using word2 cev to build a text word vector model,and calculate the distance between words for emotional tendency classification and semantic similarity Degree analysis;3)Construction of classification models--Building and comparing text-CNN and LSTM classification models,select the model with the best classification effect for experimental analysis;4)Visual display of hot words--Displaying and excavating “advanced ondemand” high-frequency vocabulary Attitudes and opinions of netizens 5)Displaying and analyzing the development trend of public opinion--Displaying and studying the development trend of public opinion at various stages with the time and volume of "advanced on-demand" topics,and at the same time,explore the development characteristics of the public opinion and the sentiment of netizens at each stage.In the end,the public opinion analysis of this "advanced on-demand" is studied from the perspective of "quantity".The experimental results prove that the LSTM model can better classify sentimental text for the topic of "advanced on-demand",and improve the classification accuracy and recall rate.And the analysis shows that the public opinion formation period of the public opinion event "I qiyi’s advance on-demand celebration for more than a year was convicted of breaking the law" was short,and the initial stage of development reached the period of high public opinion at a linear speed.In addition,the proportion of negative speech in the high period is greater than the proportion of negative emotional speech in the overall data set.