# Applying the DEMATEL - ANP Fuzzy Comprehensive Model to Evaluate Public Opinion Events
## 1 Introduction  
Leaders of nations, managers of big companies, and even heads of small families all hope to prevent small problems from developing into big ones, and the best way is to pay close attention to public opinion. Public opinion is a mirror reflecting people's will, and its development is often driven by the inner demands of netizens, triggering many public opinion incidents when mishandled, which inevitably give rise to adverse reactions from the public. The evaluation of the urgency of online public opinion can help to discover hidden social crises, quickly focus on the topics most concerning the public, understand people's demands, and quel public grievances in a timely manner. The judgment of public opinion events is usually based on human perception, the core of which is guessing and analyzing the development trend of public opinion and evaluating its degree of urgency.  
Network public opinion has been studied by many researchers to discern its law of dissemination, evolution, forecast, and propagation influence, and some mathematical and machine learning models have been discussed. The LDA-ARMA deep neural network for the dynamic presentation of public opinion events was proposed in [1], and the back propagation neural network based on the genetic algorithm (GA-BP) was employed to establish a network public opinion early warning model in [2] and an SIR model in [3]; however, the LDA-ARMA model, GA-BP, SIR model, and the Logistic model [4] utilized to classify the sentiments are too microcosmic to reflect the full picture of public opinion events, which demand a large amount of data and usually take hundreds or even thousands of learning sessions to converge even for a very simple question, and it is unable to offer support to the evaluation of public opinion events without data. The G-GETR networkmodel of online public opinion reversal based on kernel and grey degree was discussed in [5]; however, using the grey degree might limit the samples. The MIMIC-CUB model was proposed to estimate citizens' perception of the state of European economies in [6]; however, the formative part of the MIMIC model could cause confusion in interpretation. The AHP artificial weighting in [7; 8] ignores the interdependence of indicators. It was found that research on public opinion in the field of machine learning usually focuses on micro-sentiment judgment, which is unable to offer support for the evaluation of public opinion events without data and cannot evaluate events integrally. Current research from the perspective of mathematical statistics and artificial weighting has the disadvantages of sample limitation and confusion in interpretation.  
Given the above, the present paper took the fuzzy mathematical method, which cannot be limited by the number of samples and can be easily interpreted, to construct a public opinion evaluation model integrated with a decision-making model. This paper has the following structure: we first describe the construction of the index system of network public opinion based on the principles of it being easy to obtain, quantifiable, and correlated. Then, we describe the combination of the DEMATEL and ANP, which determine the weights of indicators, in which the analysis results of the DEMATEL were taken as the input of the ANP; thus, the interrelationship of the indicators was taken into consideration completely. Then, we describe how fuzzy mathematics was applied, based on which, a DEMATEL- ANP fuzzy comprehensive evaluation model was constructed and organized, as shown in Figure 1.