Research On Sentiment Analysis Algorithm Of Attention Mechanism Fusion Multi-information

In real life,with the rapid development of the Internet,more and more social platforms have emerged,such as movie websites,shopping websites,and Sina Weibo websites.People tend to express their opinions on certain content on these network platforms.Therefore,a large number of comment text containing emotional polarity have appeared,it is very important to conduct emotional polarity mining on these comment text data.Most current sentiment analysis algorithms usually used neural networks with attention mechanisms when analyzing comment text data.At the same time,fusion of other information in the attention mechanism to assist attention weight distribution is a way to improve the performance of sentiment analysis algorithms,but at present,these methods still have the problem of not making full use of other auxiliary information,resulting in poor performance of the algorithm.This paper focuses on this type of algorithm and proposes two algorithms to improve the attention mechanism,other auxiliary information is integrated into the attention mechanism to improve the weight distribution of the attention mechanism through interactive calculation,then improve the final performance of the sentiment analysis algorithm.The main research contents include:(1)Aiming at the preference problem of the attention mechanism in the existing aspect sentiment analysis algorithms,a sentiment analysis algorithm SIFAF based on the interactive fusion of aspect frequency information is proposed.First,count the frequency information of the aspect items,use the frequency information to construct the embedding vector,connect the frequency embedding vector of the aspect item and the relative position embedding vector and input them into the Bi LSTM network to extract the hidden state of the aspect frequency information,and then in the attention layer,interactively calculate the hidden state of the extracted aspect frequency information,the hidden state of the aspect item and the hidden state of the context to obtain the attention weight of each hidden state,each hidden state vector is updated according to the weight and the final representation vector of the aspect item is obtained through multiple training updates.Finally,the softmax classifier is used to calculate the emotion category of the aspect item representation vector.The experimental results on the two public data sets Restaurant and Laptop show that the algorithm in this paper works better.Compared with other benchmark algorithms,the two classification accuracy rates of the algorithm on the above two data sets were increased by 2.69% and 1.87% respectively,and the three classification accuracy rates were increased by 4.30% and 4.54% respectively..(2)Aiming at the problem that multiple types of information are simply stacked and cannot be fully utilized in existing document sentiment analysis algorithms,a sentiment analysis algorithm SIFMT based on the interactive fusion of multiple types of information is proposed.First obtain multiple types of information,use the obtained multiple types of information to construct embedding vectors,and then input various types of information embedding vectors into the Bi LSTM network to extract multiple types of information hidden states,the final attention weight is obtained by interactively calculating the extracted multi-type information hidden state and the context information hidden state in the attention layer,the context hiding information is updated according to the weight and the final document representation vector is generated.Finally,the softmax classifier is used to calculate the emotion category of the document representation vector.Experimental results on IMDB,yelp2013 and yelp2014 data sets show that the multi classification accuracy of the proposed SIFMT algorithm is improved by 10.16%,7.5% and 7.36% compared with other algorithms.Experimental results verify the effectiveness of the proposed algorithm.