Decision model of public opinion risk in campus social network based on hybrid dynamic deletion and shortest path algorithm

Aiming at the problem that traditional network public opinion monitoring and searching are inefficient and can easily cause resource waste, the study firstly, through the dynamic deletion-shortest path algorithm to classify network text, and on this basis, innovatively constructs a text sentiment cl...

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Bibliographic Details
Published in:PloS one Vol. 19; no. 11; p. e0310894
Main Authors: Xu, Nan, Wang, Yifeng
Format: Journal Article
Language:English
Published: United States Public Library of Science 18.11.2024
Public Library of Science (PLoS)
Subjects:
Accuracy
Algorithms
Artificial neural networks
Biology and Life Sciences
Classification
Computer and Information Sciences
Decision Making
Deletion
Educational aspects
Educational research
Gene deletion
Humans
Management
Neural networks
Neural Networks, Computer
Physical Sciences
Public Opinion
Recurrent neural networks
Research and Analysis Methods
School grounds
Search algorithms
Sentiment analysis
Social aspects
Social Networking
Social networks
Social organization
Social Sciences
China
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:Aiming at the problem that traditional network public opinion monitoring and searching are inefficient and can easily cause resource waste, the study firstly, through the dynamic deletion-shortest path algorithm to classify network text, and on this basis, innovatively constructs a text sentiment classification model based on the variant of convolutional neural network and recurrent neural network, and secondly, uses attention mechanism to classify the model. improvement of the classification model by using the attention mechanism. The research results show that the average precision rate, recall rate, and F-value of the dynamic deletion-shortest path algorithm are 97.30%, 79.55%, and 87.53%, and the classification speed is 397 KB / s , which is better than the traditional shortest path algorithm. In the classification effect measurement of long text, the accuracy and F-value of the recurrent neural network variant model are above 84%, and the accuracy of the text sentiment classification model with the introduction of the attention mechanism is improved by 3.89% compared to the pre-improvement period. In summary, the dynamic deletion-shortest path algorithm proposed in the study and the sentiment classification model with the introduction of the attention mechanism have superior performance and can provide certain application value for campus social network opinion risk decision-making.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0310894