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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Vydáno v:	PloS one Ročník 19; číslo 11; s. e0310894
Hlavní autoři:	Xu, Nan, Wang, Yifeng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 18.11.2024 Public Library of Science (PLoS)
Témata:	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
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0310894