Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey

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Jelodar, Hamed, et al. “Latent Dirichlet Allocation (LDA) and Topic Modeling: Models, Applications, a Survey”. Multimedia Tools and Applications, vol. 78, no. 11, 2018, pp. 15169-11, https://doi.org/10.1007/s11042-018-6894-4.
Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. (2018). Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimedia Tools and Applications, 78(11), 15169-15211. https://doi.org/10.1007/s11042-018-6894-4
Jelodar H, Wang Y, Yuan C, Feng X, Jiang X, Li Y, et al. Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimedia Tools and Applications. 2018;78(11):15169-211.
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Category Category Repetition
Science: Mathematics: Instruments and machines: Electronic computers. Computer science137
Science: Science (General): Cybernetics: Information theory91
Technology: Engineering (General). Civil engineering (General)85
Social Sciences76
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics67
The first research to cite this article was titled Topic Detection Approaches in Identifying Topics and Events from Arabic Corpora and was published in 2018. The most recent citation comes from a 2024 study titled Topic Detection Approaches in Identifying Topics and Events from Arabic Corpora . This article reached its peak citation in 2023 , with 182 citations.It has been cited in 342 different journals, 22% of which are open access. Among related journals, the IEEE Access cited this research the most, with 23 citations. The chart below illustrates the annual citation trends for this article.
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