Deep adversarial neural network model based on information fusion for music sentiment analysis

Abstract

Natural language processing (NLP) is a computer-based technology used to process natural language information in written and spoken form that is unique to human society. In the process of mining massive text information, a variety of technologies and research directions in the field of NLP have gradually emerged. And sentiment analysis is an important research direction, which has important research value and practical application value for enterprises and social life. Sentiment analysis is basically a single mining of semantic or grammatical information without establishing the correlation between semantic information and grammatical information. In addition, previous models simply embed the relative distance or grammatical distance of words into the model, ignoring the joint influence of relative distance and grammatical distance on the aspect words. In this paper, we propose a new model that combines deep adversarial neural network model based on information fusion for music sentiment analysis. Firstly, the information of music text sequence is captured by the bidirectional short and long time memory network. Then the sequence information is updated according to the tree structure of dependency syntactic tree. Then, the relative distance and syntactic distance position information are embedded into the music text sequence. Thirdly, the adversarial training is used to expand the alignment boundary of the field distribution and effectively alleviate the problem of fuzzy features leading to misclassification. Semantic information and syntactic information are optimized by attention mechanism. Finally, the fused information is input into the Softmax classifier for music sentiment classification. Experimental results on open data sets show that compared with other advanced methods, the recognition accuracy of the proposed method is more than 90%.