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

Xiaojie Li

doi:10.1515/jisys-2021-0088

Abstract

AbstractThrough 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, andF-value in classifying emotional tendency of web texts, followed by the traditional CNN model and the SVM model.

Highlights

With the development of the Internet, people are acquiring information faster and faster, and the amount of information they acquire is increasing
Online public opinion is a kind of group opinion, which is composed of different individual opinions
This paper briefly introduced the basic model structure used for emotion analysis of online public opinion and the convolutional neural network (CNN) model used for text emotion classification

Summary

Introduction

With the development of the Internet, people are acquiring information faster and faster, and the amount of information they acquire is increasing. Online public opinion refers to the tendentious and influential common opinions formed by the public on certain social events through the Internet. Due to the complexity of interest relationships, online public opinion is likely to cause dilemmas for individuals, enterprises, or governments [2]. Online public opinion caused by false or one-sided untrue information can cause serious adverse consequences. Online public opinion is a kind of group opinion, which is composed of different individual opinions. Different individual opinions have different emotional connotations, they converge to show emotional tendencies as a whole, reflecting most people’s emotional feedback on the event [3]. Through the analysis of emotional tendencies in online public opinion, we can effectively understand the impact of public policies or events involving public interests on the masses, and take targeted measures. Xu et al [4] proposed a term weighting method based on improved term frequency-inverse document frequency for

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Conclusion