Research On Online Public Opinion Based On Semantic Analysis

In recent years,the number of Internet users in China has been increasing year by year.Internet users vent their emotions and express their aspirations through network platform,making higher enthusiasm and participation frequency of government and businesses.However,various social contradictions that emerged during this social transformation period are often concentrated on the network platform and form a powerful platform for public opinion.The rapid development of Web2.0 has made the Internet a major communication channel for cultural thoughts and public voices.Meanwhile,the data with daily large-scale growth has also brought great difficulties to the management and situation judgement of the network.Therefore,based on the research results of scholars at home and abroad,this paper combines multidisciplinary theories and techniques such as machine learning,information processing,news communication,natural language processing,and data mining,a multi-angle study was conducted from approach theory,technology and application of network public opinion analysis.The research content and innovations of this paper include the following parts:The first part(corresponding to the second chapter)The theoretical system of network public opinion analysis method is constructed and explained,that is,the network public opinion analysis method system based on basic method as reference,common analysis method as support and modern intelligent information processing method as guidance.The text content mining method,intelligent analysis method,network measurement analysis method,etc.were introduced in detail.Taking the hot news event of “Changsheng Vaccine” in online public opinion as an example,content analysis method was used to conduct experimental analysis and research on the relevant report of the event.In terms of intelligent analysis,this paper gave detailed concepts and algorithm ideas for text content mining and topic structure mining and their application in network public opinion.By comparing and analyzing various clustering algorithms,the deficiency of single-pass incremental clustering algorithm in text clustering would be conquered by introducing seed topics.The newly added documents only need to be compared with the seed topics in the cluster,and in the process of comparison,the seed topic would be constantly updated and improved.The second part(corresponding to the third chapter)On the basis of the secondchapter,the intelligent analysis method in the network public opinion analysis method system is further improved.This part focuses on semantic analysis based on external semantic knowledge and latent semantic analysis(LSA).Firstly,based on the semantic analysis of external semantic knowledge,this paper takes CNKI as an example and uses it as the semantic knowledge resource of the system to analyze the similarity calculation method based on CNKI semantic dictionary.The similarity calculation method between words only considers the distance factor between primaries,and does not consider the influence of the primary depth on similarity calculation.Influence factor and depth influence factor are improved with the introduction of primary relative position and are applied in the process of calculating sentence similarity and paragraph similarity.The improved algorithm is more accurate and relevant by relevant experimental verification.Secondly,introduces the principle of the latent semantic analysis(LSA)and computational method of singular value decomposition(SVD).The paper proposes two methods to give the SVD singular values of k,in view of that if k quantity is too large,the generated semantic space and the original vector space model is highly similar,and if k quantity is too small,it will lose useful information caused by the problem of semantic useful structure in the space is too little.One is to determine the value by manual adjustment in the process of experiment,and the other is set the maximum principal factor of k text based on the theory of reference factor analysis.The paper gives the specific dimension reduction steps.Finally,after analyzing the insufficiency of text representation and local area generation method of LSA method,the paper proposes the method of using locality of text to class as a local area generation method R-LLSA,and using SVM classifier to obtain the relevance parameter of text pair category and apply it to the local space generation process.By comparing with C-LLSA and GL-LSA classification results,it is shown that R-LLSA is a more efficient text classification representation method,and the required feature dimension parameters are the smallest,which further optimizes SVD process.The third part(corresponding to the fourth chapter)The sentiment orientation analysis is divided into word level,sentence level,paragraph level and chapter level according to the granularity of the processed text,and focuses on the two methods of emotional orientation analysis of the minimum granularity,namely words: based on dictionary and based on corpora.Because the emotional orientation judgment problemcan be transformed into serial annotation problems,the main algorithm ideas and deficiencies of CRFs model,related annotation model,were discussed.The fourth part(corresponding to the fifth chapter)The connotation and characteristics of online public opinion events are analyzed.The frequency,trend,netizen group behavior and evolution characteristics of online public opinion events are sorted out and analyzed.The "Changsheng Vaccine" event is taken as an example and regression analysis in trend analysis method is used to explore the trend analysis of attention degree of public opinion events and discuss various stages of the evolution of public opinion.Mainly through selecting 100,000 pieces of data on Sina Weibo from July 22 to 29,2018 and using it as training data.After complete a series of processing such as data noise filtering,disambiguation and de-duplication,1200 pieces of active period(50 hours)were obtained.The data,using this as a sample,constructs a regression model of two indicators of public opinion attention(Weibo number and Weibo users number),and generates corresponding trend lines to get the appropriate regression function and maximum fitting value,so it can predict the Weibo users' attention trend in the whole public opinion event.Based on the modeling idea of Weisbuch-Deffuant model,individual view interaction rules in the evolution of network public opinion are proposed in this part with the combination of particularity of network public opinion generation and the heterogeneity of individual opinion acceptance.Through simulation experiments,the distribution of individual opinion acceptance degree ?,trust threshold ? and the influence of opinion leaders on the evolution of network public opinion are analyzed.Experimental results show that compared with ? random distribution,when ? obeys the normal distribution with a mean of 0.5,network public opinion will converge at a faster rate,and there will be a group polarization effect on the evolution of network public opinion when there is an opinion leader.