<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, the dynamic tracking technique is applied to analyze the triggering factors of online public opinion, modify the feature subsets and establish a basic database of sensitive information. The test of statistical characteristics of the evolution process of public opinion is realized by using the prediction of time series by the ANOVA algorithm, and on this basis, the smoothness of the model is realized by testing the parameters. In the process of establishing the quantitative index system, the seasonal difference algorithm is introduced to predict the development trend of public opinion on hot topics to realize the visualization model design. Finally, in order to verify the validity of the model, an experiment was conducted with Event A as an example. The results show that users can set a total of 4 keywords with an average word frequency of 0.03 Hz. The prediction accuracy in different periods can reach 88.73% on average, and the adaptation value can reach 0.8 percentage points on average. Thus, it can be seen that the trend analysis model of online public opinion evolution constructed in this paper can quantitatively analyze the mechanism of public opinion segmentation evolution and the trend prediction problem and provide a theoretical reference for the government to govern public opinion.</p>
</abstract>

In this paper, the dynamic tracking technique is applied to analyze the triggering factors of online public opinion, modify the feature subsets and establish a basic database of sensitive information. The test of statistical characteristics of the evolution process of public opinion is realized by using the prediction of time series by the ANOVA algorithm, and on this basis, the smoothness of the model is realized by testing the parameters. In the process of establishing the quantitative index system, the seasonal difference algorithm is introduced to predict the development trend of public opinion on hot topics to realize the visualization model design. Finally, in order to verify the validity of the model, an experiment was conducted with Event A as an example. The results show that users can set a total of 4 keywords with an average word frequency of 0.03 Hz. The prediction accuracy in different periods can reach 88.73% on average, and the adaptation value can reach 0.8 percentage points on average. Thus, it can be seen that the trend analysis model of online public opinion evolution constructed in this paper can quantitatively analyze the mechanism of public opinion segmentation evolution and the trend prediction problem and provide a theoretical reference for the government to govern public opinion.

Analysis of the Evolution Trend of Segmentation of Online Public Opinion Based on Dynamic Tracking Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, the dynamic tracking technique is applied to analyze the triggering factors of online public opinion, modify the feature subsets and establish a...

Analysis of the Evolution Trend of Segmentation of Online Public Opinion Based on Dynamic Tracking Technology

Published Online: Oct 09, 2023

Page range: -

Received: Dec 27, 2022

Accepted: Apr 19, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00540

Keywords
Dynamic tracking technology, Public opinion trend analysis, Analysis of variance, Seasonal differencing, Feature subset, Visualization model

© 2023 Zhen Gong, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of the Evolution Trend of Segmentation of Online Public Opinion Based on Dynamic Tracking Technology

Published Online: Oct 09, 2023

Page range: -

Received: Dec 27, 2022

Accepted: Apr 19, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00540

KeywordsDynamic tracking technology, Public opinion trend analysis, Analysis of variance, Seasonal differencing, Feature subset, Visualization model

© 2023 Zhen Gong, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Dynamic tracking technology, Public opinion trend analysis, Analysis of variance, Seasonal differencing, Feature subset, Visualization model