Research On Sentiment Analysis Based On Dependency Parsing And Deep Neural Network

With the rapid progress of information technology on today’s Internet,people can obtain valuable content from the vast number of textual materials daily,from shopping,movies to current events,and how to extract valuable sentiment information from them has become a hot topic today,and text sentiment analysis has developed into an important player in the field of natural language processing.In recent years,the development of pre-trained models has led to significant improvements in accuracy and reliability on most text sentiment analysis datasets.Although traditional text coding techniques can effectively process text sequences,further research is needed to make full use of the syntactic structure information of texts.To this end,this paper proposes a deep neural network model that depends on the syntactic structure and the contextual semantic information of the text fused by an adaptive attention mechanism and explores and analyzes its predictive effect on sentiment analysis tasks through experiments.The specific research work in this paper is divided into two main parts as follows:(1)In order to improve the unsupervised dependency syntactic analysis model,the UD-Tree BERT model is proposed for the problem of difficulty in obtaining long-span dependency relations in text.The unsupervised syntactic analysis model is used to obtain a more reasonable and linguistically appropriate dependency structure.The model is compared and analyzed with the supervised dependency syntactic analysis model and the unsupervised syntactic analysis model in the experiment.(2)This paper proposes a BERT-Adaptive Attention based sentiment analysis model for the text sentiment analysis task.The model incorporates the dependent syntactic structure feature information output from the above UD-Tree BERT model into the Adaptive Attention sentiment analysis model proposed in this paper.The model applies the text-implicit dependent syntactic structure information to the sentiment analysis task,so that the BERT model obtains further improvement in the prediction performance of the sentiment analysis task.In comparison experiments with current state-of-the-art sentiment analysis models on the Online＿Shopping＿10＿Cats dataset,a significant improvement was eventually achieved,which shows the value of adding additional dependent syntactic analysis information to the model to effectively handle the syntactic information of more complex sentences.