Research on the emotional tendency of web texts based on long short-term memory network

Abstract

Through the analysis of emotional tendency in online public opinion, governments and enterprises can stabilize people’s emotion more effectively and maintain social stability. The problem studied in this paper is how to analyze the emotional tendency of online public opinion efficiently, and finally, this paper chooses deep learning algorithm to perform fast analysis of emotional tendency of online public opinion. This paper briefly introduced the structure of the basic model used for emotional tendency analysis of online public opinion and the convolutional neural network (CNN) model used for text emotion classification. Then, the CNN model was improved by long short-term memory (LSTM). A simulation experiment was carried out on MATLAB for the improved text emotion classification model to verify the influence of activation function type on the improved model and the performance difference between the improved model and support vector machine (SVM) and traditional CNN models. The results showed that the improved classification model that adopted the sigmoid activation function had higher accuracy and was less affected by language than the relu and tanh activation functions; the improved classification model had the highest accuracy, recall rate, and F-value in classifying emotional tendency of web texts, followed by the traditional CNN model and the SVM model.