Application of edge computing combined with deep learning model in the dynamic evolution of network public opinion in emergencies

Abstract

The aim is to clarify the evolution mechanism of Network Public Opinion (NPO) in public emergencies. This work makes up for the insufficient semantic understanding in NPO-oriented emotion analysis and tries to maintain social harmony and stability. The combination of the Edge Computing (EC) and Deep Learning (DL) model is applied to the NPO-oriented Emotion Recognition Model (ERM). Firstly, the NPO on public emergencies is introduced. Secondly, three types of NPO emergencies are selected as research cases. An emotional rule system is established based on the One-Class Classification (OCC) model as emotional standards. The word embedding representation method represents the preprocessed Weibo text data. Convolutional Neural Network (CNN) is used as the classifier. The NPO-oriented ERM is implemented on CNN and verified through comparative experiments after the CNN's hyperparameters are adjusted. The research results show that the text annotation of the NPO based on OCC emotion rules can obtain better recognition performance. Additionally, the recognition effect of the improved CNN is significantly higher than the Support Vector Machine (SVM) in traditional Machine Learning (ML). This work realizes the technological innovation of automatic emotion recognition of NPO groups and provides a basis for the relevant government agencies to handle the NPO in public emergencies scientifically.

Keywords: CNN; Deep learning; Emotion recognition; Network public opinion; Public emergency.

Fig. 1

Emotional and cognitive mechanism of the OCC model

Fig. 2

Emotional category mapping of NPO

Fig. 3

The impact of word vector dimensions on the proposed NPO-oriented ERM

Fig. 4

The influence of the convolution kernel size on the NPO-oriented ERM

Fig. 5

The influence of the number of convolution kernels on the NPO-oriented ERM

Fig. 6

The impact of Dropout on the proposed NPO-oriented ERM

Fig. 7

The influence of the L2 norm on the proposed NPO-oriented ERM

Fig. 8

The influence of mini-batch size on the proposed NPO-oriented ERM

Fig. 9

The impact of optimized hyperparameters on the classification performance of proposed NPO-oriented ERM based on CNN

Fig. 10

The impact of OCC emotion labeling on the proposed NPO-oriented ERM

Fig. 11

The influence of the text representation method on the proposed NPO-oriented ERM

Fig. 12

The influence of emotion classifier on the proposed NPO-oriented ERM