  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.
