Sentiment Tendency Of Internet Public Opinion Based On Deep Memory Network

In recent years,the traditional public opinion analysis technology has become a very mature application.However,with the rapid iterative update of network culture and the emergence of a large number of irony and implied emotions in the political and economic fields,traditional public opinion analysis often fail.This demand for recognizing irony or implied sentiment is more valuable when artificial intelligence and natural language processing are booming.In view of the problem that traditional network public opinion analysis is influenced by network culture and implicit emotions,this paper proposes an effective and feasible idea:1)Firstly,a method based on deep memory network is studied to solve the problem of sentiment recognition in public opinion analysis.The experimental results show that the proposed model can effectively solve the hidden emotions and improve the confidence of the prediction results.2)Then,by referring to the word vector mechanism,the Emoji in the sample is supervised and pre-trained,and the Emoji expression is used to construct the feature vector.On the IMDB,and the improved model obtains 88.2%classification accuracy.3)Then take into account the inevitable dependence of the public opinion analysis system on the keywords,make full use of the emotion dictionary,and constrain the prediction result probability through the adaptive threshold to constrain and fine tune.And the accuracy of about 90.7%is obtained on the Chinese data set,which verifies the effectiveness of the method applied in the public opinion.4)Finally,a system that can better realize network public opinion crawling,implicit emotional tendency identification and analysis result visualization is constructed.