Research on the Path of Network Opinion Expression in AI Environment for College Students.
Journal
Computational and mathematical methods in medicine
ISSN: 1748-6718
Titre abrégé: Comput Math Methods Med
Pays: United States
ID NLM: 101277751
Informations de publication
Date de publication:
2021
2021
Historique:
received:
27
10
2021
revised:
11
11
2021
accepted:
20
11
2021
entrez:
20
12
2021
pubmed:
21
12
2021
medline:
19
2
2022
Statut:
epublish
Résumé
Network interaction has evolved into a grouping paradigm as civilization has progressed and artificial intelligence technology has advanced. This network group model has quickly extended communication space, improved communication content, and tailored to the demands of netizens. The fast growth of the network community on campus can assist students in meeting a variety of communication needs and serve as a vital platform for their studies and daily lives. It is investigated how to extract opinion material from comment text. A strategy for extracting opinion attitude words and network opinion characteristic words from a single comment text is offered at a finer level. The development of a semiautonomous domain emotion dictionary generating technique improves the accuracy of opinion and attitude word extraction. This paper proposes a window-constrained Latent Dirichlet Allocation (LDA) topic model that improves the accuracy of extracting network opinion feature words and ensures that network opinion feature words and opinion attitude words are synchronized by using the location information of opinion attitude words. The two-stage opinion leader mining approach and the linear threshold model based on user roles are the subjects of model simulation tests in this study. It is demonstrated that the two-stage opinion leader mining method suggested in this study can greatly reduce the running time while properly finding opinion leaders with stronger leadership by comparing the results with existing models. It also shows that the linear threshold model based on user roles proposed in this paper can effectively limit the total number of active users who are activated multiple times during the information diffusion process by distinguishing the effects of different user roles on the information diffusion process.
Identifiants
pubmed: 34925540
doi: 10.1155/2021/4360792
pmc: PMC8674046
doi:
Types de publication
Journal Article
Retracted Publication
Langues
eng
Sous-ensembles de citation
IM
Pagination
4360792Commentaires et corrections
Type : RetractionIn
Informations de copyright
Copyright © 2021 Yue Zhu and Muhammad Talha.
Déclaration de conflit d'intérêts
The authors declare that they have no conflicts of interest.
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