Stuctured Processing And Topic Mining Of Social Media Knowledge About Public Transportation

With the development of the Internet and the rapid popularization of social platforms,the information of today's society has become more and more obvious,and the data on the network platform has also shown a trend of massive growth.It is difficult for users to obtain reliable information accurately and quickly on the network platform.In order to help users quickly obtain the content of topics that are closely related to life but easily overlooked from social platforms,this research designed a data structured processing method and network topic mining algorithm for social platforms,and used an approximate algorithm for topic mining algorithm.Validity is verified.This article mainly introduces several aspects of data structure processing,network topic extraction and algorithm verification.Firstly,it introduces the mining and structured processing of data on social networking platforms.The data used in this study comes from heterogeneous data related to public transportation generated by users on the Weibo social platform;the main task of data structuring is to structure user attributes,user-generated text data and user published content attributes,and use ranking algorithms,weight reconstruction and multi-modal data fusion methods to construct a keyword map matrix to achieve the purpose of enhancing the topic quality of the data set and enhancing the semantic relevance between keywords.Secondly,a network topic extraction algorithm is introduced.The research idea of using the poisson deconvolution integral solution algorithm in this study is to extract the semantic relationship between keywords and topics by de-noising and feature extraction on the keyword map matrix,and sort the topics according to the degree of topic attention.For the excavated topic content,the topic quality evaluation and verification are carried out from both subjective and objective perspectives to ensure the accuracy and reliability of the topic content.Finally,a method of approximating the objective function is introduced.In order to verify the effectiveness of the poisson deconvolution and integration solution algorithm,a laplace approximation algorithm was used to approximate the initial function of the Poisson deconvolution and integration solution algorithm used in this research,and finally without changing the physical meaning of the algorithm.Next,a new objective function is obtained.The new objective function is optimized to solve and topic mining,and the mining results are compared with the mining results of the poisson deconvolution and integral solution algorithm,which proves the effectiveness of the poisson deconvolution and integral solution algorithm used in this study.