BiLSTM-CNN Text Emotion Analysis Based on Self Attention Mechanism and Dense Connection

Abstract

In order to improve the accuracy of sentiment analysis, this paper presents a new method for sentiment analysis of BiLSTM-cnn text based on self attention mechanism and dense connection. Methods BiLSTM-CNN-Attmodel was established. Firstly, BiLSTM was introduced to extract context words. Then, the convolutional neural network(CNN) is used to extract local semantic features. Combined with DenseNet dense connection module, the memory strength of the whole model is improved, and the utilization rate of weight information is enhanced. Finally, Self-attention mechanism is used to improve the ability of model mining information. This paper selects the data sets of chndenticorp and CCF2012 to train the optimal value of the DenseNet feature mapping matrix. The optimal value is brought into the model contrast experiment. In the experiment, the accuracy rate, recall rate and F value of this method are all greater than 91%, which is the highest among the models. It effectively improves the accuracy of text sentiment analysis, and has high research and practical value.