Research On Crisis Sentiment Classification Of Social Network Comments Based On LSTM And Self-Attention

In recent years,crisis events occur frequently in the world.Whether due to natural disasters or man-made factors,they all have a great impact on the physical and mental health of the public,especially a series of emotional reactions such as strong fear and panic.Coupled with the casual nature of today's social networks,it has created a mass panic.Therefore,it is very important to effectively manage and control the public's crisis mood in public emergencies.Nowadays,social networks are highly developed and an important platform for the public to express their emotions.Many events in the world are constantly fermenting and related to the spread and group gathering of social networks.Therefore,it is of great importance to effectively identify and classify the potential crisis emotions on social networks.In this paper,LSTM(Long and Short Term Memory Network)and Self-Attention(Self-Attention Mechanism)were used to design the model,and a crisis emotion classification dictionary was constructed based on frame semantics,so as to achieve the purpose of accurately classifying crisis emotion.The main work completed in this paper is as follows:First of all,the literature related to the classification of crisis emotions at home and abroad is sorted out,and the concept and related theories of crisis emotions are clarified.Then,on the basis of theoretical analysis combined with the survey of social network comment data,a classification system suitable for crisis sentiment analysis is established.Based on the framework semantic theory and the sina weibo user comment text as the research object,the Chinese crisis sentiment classification dictionary is constructed.After analyzing the advantages and existing problems of the neural network algorithm,an optimization model of crisis emotion classification combining LSTM and self-attention was built.Through the experiment of large-scale social network comment text,this paper tests the effects of different combinations of LSTM,emotion dictionary,self-attention and other methods on crisis emotion classification.The results show that the classification accuracy of model with emotion dictionary and self-attention mechanism is significantly higher than that of LSTM model alone.The research results of this paper provide reference for the related research on crisis emotion,and provide technical support for the management and prevention of crisis events.