Application Research Of Text Content Monitoring And Analysis Based On Word2vec And SVM

With the rapid development of the mobile internet and the rapid growth of intelligent terminal users,instant messaging tools such as SMS,QQ,and WeChat are widely used.They produce a lot of text content,which contains a lot of complex bad text information.Through monitoring and analyzing these bad texts,the degree of purification of the network environment can be effectively improved.The traditional content-based monitoring and analysis is mainly the software automatic monitoring plus manual auditing governance.The automatic monitoring method is mainly based on the text classification technology of machine learning related algorithms,but these classification technologies have gradually been unable to adapt to more complex big data environments and application scenarios.In particular,the continuous improvement of natural language processing technology and the development of distributed computing applications have put forward higher requirements for traditional text content monitoring and analysis.Therefore,this paper analyzes the relevant theories and techniques of text classification in detail,proposes a text classification model based on word2 vec and SVM,and applies it to short message text content monitoring and analysis problems.Aiming at the bad text of short message,the use of word vector generated by word2 vec can effectively preserve the correlation between words,this paper proposes a method of feature extraction of short message,combining with the distributed parallel SVM model,to solve the actual demand of text classification of short message.The experiment proves that it has better comprehensive performance.Design and implement a monitoring and analysis system of short message content.At present,the application system has been put into practical operation and has achieved good monitoring and analysis results,which has provided assistance for the management of telecommunication bad information.According to the existing literature search,no application system similar to the method of this paper has yet been found.