Sentiment Analysis Of Weibo Image And Text Based On Multi-feature Fusion

In recent years,the rapid development of social media such as Sina Weibo and Twitter has reduced the communication cost between people and has become one of the main ways for people to express their emotions.The multimedia fusion method with pictures and texts is especially loved by people.They share their views and feelings about things on Weibo actively.Positive energy emotion is beneficial to social harmony,on the contrary,negative energy emotion will bring some harm to society.Therefore,the research on Weibo sentiment analysis is helpful to the supervision and guidance of public opinion,and has practical application value.In addition,Weibo is also used in marketing,and sentiment analysis of these information can better obtain user interest,which helps businesses improve service and improve commodity quality.This paper focuses on the sentiment analysis of Weibo,the main research content and contribution are as follows:(1)This paper studies Weibo sentiment analysis method based on user portrait.The existing methods of Weibo sentiment analysis seldom consider the influence of user personalized emotional expression difference and user emotion easily affected by others.Therefore,this paper proposes a Weibo sentiment analysis method based on user portraits.First,according to the differences of personal emotion expression of Weibo users,we obtain the user's personality characteristics and influence based on the historical Weibo and personal profile of Weibo users to construct user portraits for each user.Then,combined with Weibo sentences and user portraits,a multi feature text sentiment analysis model is constructed to obtain the sentiment of Weibo text.The experimental results show that user portrait can effectively improve the performance of emotion analysis,and the accuracy and F value of the model are improved by 5.24%and 5.39% respectively compared with the model without user portrait.(2)This paper studies the method of automatically expanding the scope of sentiment lexicon.Aiming at the problem of limited capacity and poor adaptability of sentiment lexicon,a word replacement model based on sentiment lexicon is proposed.In this model,when the emotion words to be searched do not exist in the sentiment lexicon,they are replaced by their similar emotion words,which expands the scope of the sentiment lexicon,and this method can be applied to most sentiment lexicons.Specifically,we first use the bidirectional long-term and short-term memory network combined with attention mechanism to obtain important information in the context,and then design a word replacement model to obtain the words closest to the target words in the lexicon,so as to achieve word replacement.The experimental results show that the model can better expand the scope of sentiment lexicon,and the accuracy and F value of emotion analysis are improved by 2.19% and 1.5% respectively compared with the model without using word replacement.(3)This paper studies the sentiment analysis method of Weibo image and text fusion.At present,Weibo users prefer to express their views and feelings in the way of both pictures and texts.It is difficult to accurately obtain the real feelings of users by analyzing the text or image.Therefore,a method of Weibo sentiment analysis based on Bert is proposed.Firstly,the text feature and image feature representation are obtained by using widely used Bert and Res Net.Then,these features are input into the improved Bert model.Finally,the final emotional polarity of Weibo is obtained through the full connection layer.In the improved Bert model,we introduce residual network and multi attention mechanism to make up for the loss of feature extraction caused by too many layers of Bert,and capture more emotional information in graphic features.The experimental results show that the method based on Bert is effective,and the accuracy and F value of the method are 4.43% and 6.18% higher than the existing methods.