Research On Hot Topics And Propagation Behavior Prediction Of Online Public Opinion On Agricultural Products

These information about the safety and price of agricultural products are closely related to the livelihood and health of the general public,it is easier to cause widespread concern and heated discussion on network platforms.How to timely and accurately grasp the hot issues of online public opinion,dig out opinion leaders with greater influence,accurately predict the propagation path of netizens in public opinion,understand the public’s concerns,scientifically evaluate the development of public opinion,and effectively prevent and control potential risks of public opinion.It is a scientific research problem with important theoretical significance and practical application value.This paper uses text mining,social network analysis and deep learning and other technologies and to conduct a comprehensive and systematic research and analysis on the hot topics,themes evolution,user’s propagation behavior and key paths of online public opinion on agricultural products.Firstly,the improved social network analysis method is used to realize the hot spot discovery and topic discovery of agricultural network public opinion events,and then the opinion leaders are mined according to the user’s repost behavior,and an improved public opinion propagation prediction model is constructed to realize the prediction of user’s repost behavior and identification of critical paths of public opinion.In this paper,the case analysis and model application of the "Salmon Covid-19" public opinion event in recent years are carried out,and the validity and practicability of the model analysis method in this paper are demonstrated.The specific research content and results of this paper are as follows:(1)We comprehensively use social network and dependent syntax rules to analyze public opinion texts,and realizes the hot topics discovery of agricultural network public opinion and the visual analysis of themes evolution.This kind of public opinion text data is semantically analyzed according to the dependency syntax rules,and the social network method is used to realize the discovery and visual analysis of public opinion hot topics.According to the evolution of hot words and their network topology in the process of public opinion development,we realized the reasonable division of public opinion life cycle and the accurate extraction of public opinion topics.Combined with the characteristics of the number of nodes and node degrees of social networks,we quantified the evolution law of hot topics of public opinion to analyze.The empirical analysis of the "Salmon COVID-19" event shows that the text analysis method in this paper can deeply excavate comprehensive and clear hot topics from a large amount of short text information.The analysis provides effective analytical methods and techniques.(2)An improved deep learning model is used to realize the prediction of netizens’ repost behavior of public opinion information.We constructed an improved Transformer model---LTR-Transformer.The innovation of this model is mainly reflected in three aspects: Firstly,for the problem of long text on Weibo,we discarded the Decoder part used for generating tasks in the original model,and the Encoder part and deep fully connected layer are constructed to build a classification model based on the original model.We combined the features of the segmented part of previous text into the input of the next text segment to improve the robustness of the model to long texts.Secondly,we use relative positions for encoding,which solves the problem of duplication of position information between two texts.Thirdly,we propose the MSRFS feature intersection method.On the basis of one-dimensional splicing features,we added twodimensional intersection features as model inputs to improve the model’s ability to express text features.By analyzing the empirical case of agricultural network public opinion event "Salmon Covid-19",the results show that,compared with the classic deep learning models,the LTR-Transformer model achieves more accurate prediction of public opinion propagation behavior in a shorter convergence time.This rescerch lay the foundation for the real-time monitoring and rapid identification of propagation key paths of online public opinion.(3)The optimized Page Rank algorithm is used to achieve accurate mining of opinion leaders of public opinion events,and combined with the repost behavior predicted by the LTRTransformer model,it realizes the identification of the key propagation path of public opinion.Combined with the analysis of the content and nature of public opinion events,and comprehensively considering various important factors affecting the attitude of netizens and the evolution trend of public opinion,we propose an opinion leader evaluation index system and an opinion leader mining model LATA.The model comprehensively considers leadership,activity,topological potential and related the influence of degree on opinion leaders,combined with the prediction results of propagation behavior,it realizes the identification of the key propagation paths of public opinion.The comparative experimental results and case analysis show that the opinion leader mining algorithm proposed in this paper has higher accuracy and coverage than other models,it can quickly and accurately identify opinion leaders,and quickly identify and predict public opinion events through the key propagation paths of.The propagation paths and diffusion range can be effectively controlled and information guidance can be achieved.Aiming at the practical needs of effective supervision of agricultural network public opinion,this paper realizes the mining of network public opinion hot topics and the visual analysis of the evolution trend.Accurate identification of the key paoropagation paths of public opinion helps to quickly and accurately identify the hot topics and changes of public opinion events,and accurately predict the propagation direction and scope of public opinion.The research in this paper further enriches and develops the modeling methods and quantitative analysis methods of agricultural network public opinion,which helps to reveal and discover the law of the occurrence and development of agricultural network public opinion,and provides a scientific basis for scientific public opinion supervision and risk prevention and control.