Implicit Sentiment Analysis for Chinese Texts Based on a Hybrid Neural Network

Abstract:

Implicit sentiment analysis is an important part of emotional computing, especially sentiment analysis based on deep learning has become a research hotspot in recent years. This paper uses convolutional neural network to extract features from text, combines longshortterm memory network (LSTM) structure to extract context information, and adds attention mechanism to the network to construct a new hybrid neural network model to realize implicit emotions analysis on text. The hybrid neural network model extracts more meaningful semantic features such as sentence semantics and structure from the hierarchical structure of word level and sentence level respectively, attention is paid to the characteristics of large emotional contribution rate through attention mechanism. The proposed model has a classification accuracy of 77% on the public implicit sentiment data set and can improve the effect of text emotion analysis more comprehensively.