Rating the Crisis of Online Public Opinion Using a Multi-Level Index System

TL;DR

This paper presents a multi-level index system combined with deep learning and grey correlation analysis to objectively evaluate and rate the crisis level of online public opinion, aiding in timely crisis warning.

Contribution

It introduces a novel multi-level index framework integrating emotion classification and correlation analysis for online opinion crisis assessment.

Findings

Effective in real-time incident evaluation

Accurately quantifies emotional tendencies

Assists in early crisis warning

Abstract

Online public opinion usually spreads rapidly and widely, thus a small incident probably evolves into a large social crisis in a very short time, and results in a heavy loss in credit or economic aspects. We propose a method to rate the crisis of online public opinion based on a multi-level index system to evaluate the impact of events objectively. Firstly, the dissemination mechanism of online public opinion is explained from the perspective of information ecology. According to the mechanism, some evaluation indexes are selected through correlation analysis and principal component analysis. Then, a classification model of text emotion is created via the training by deep learning to achieve the accurate quantification of the emotional indexes in the index system. Finally, based on the multi-level evaluation index system and grey correlation analysis, we propose a method to rate the crisis of online public opinion. The experiment with the real-time incident show that this method can objectively evaluate the emotional tendency of Internet users and rate the crisis in different dissemination stages of online public opinion. It is helpful to realizing the crisis warning of online public opinion and timely blocking the further spread of the crisis.