The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
A Survey of Topic Modeling in Text Mining
Open accessAbstract
Topic models provide a convenient way to analyze large of unclassified text. A topic contains a cluster of words that frequently occur together. A topic modeling can connect words with similar meanings and distinguish between uses of words with multiple meanings. This paper provides two categories that can be under the field of topic modeling. First one discusses the area of methods of topic modeling, which has four methods that can be considerable under this category. These methods are Latent semantic analysis (LSA), Probabilistic latent semantic analysis (PLSA), Latent Dirichlet allocation (LDA), and Correlated topic model (CTM). The second category is called topic evolution models, which model topics by considering an important factor time. In the second category, different models are discussed, such as topic over time (TOT), dynamic topic models (DTM), multiscale topic tomography, dynamic topic correlation detection, detecting topic evolution in scientific literature, etc.
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Cites
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Blei, D. M., Ng, A. Y., & Jordan, M. I. (2002) · Advances in Neural Information Processing Systems 14 · doi:10.7551/mitpress/1120.003.0082 · Cited by 12,223
Blei, D. M. (2012) · Communications of the ACM · doi:10.1145/2133806.2133826 · Cited by 5,516
Unsupervised Learning by Probabilistic Latent Semantic Analysis
Hofmann, T. (2001) · Machine Learning · doi:10.1023/a:1007617005950 · Cited by 2,456
Blei, D. M., & Lafferty, J. D. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143859 · Cited by 2,386
Topics over time: a non-Markov continuous-time model of topical trends
Wang, X., & McCallum, A. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150450 · Cited by 1,121
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Fast collapsed gibbs sampling for latent dirichlet allocation
Porteous, I., Newman, D., Ihler, A., Asuncion, A., Smyth, P., & Welling, M. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1401960 · Cited by 560
Topic and Role Discovery in Social Networks with Experiments on Enron and Academic Email
McCallum, A., Wang, X., & Corrada-Emmanuel, A. (2007) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2229 · Cited by 447
Joint latent topic models for text and citations
Nallapati, R. M., Ahmed, A., Xing, E. P., & Cohen, W. W. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1401957 · Cited by 408
Cited by
Smart literature review: a practical topic modelling approach to exploratory literature review
Asmussen, C. B., & Møller, C. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0255-7 · Cited by 455
Curiskis, S. A., Drake, B., Osborn, T. R., & Kennedy, P. J. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2019.04.002 · Cited by 304
Topic modeling algorithms and applications: A survey
Abdelrazek, A., Eid, Y., Gawish, E., Medhat, W., & Hassan, A. (2023) · Information Systems · doi:10.1016/j.is.2022.102131 · Cited by 304
Topic Modeling: A Comprehensive Review
Kherwa, P., & Bansal, P. (2018) · ICST Transactions on Scalable Information Systems · doi:10.4108/eai.13-7-2018.159623 · Cited by 264
A survey on knowledge-oriented retrieval-augmented generation
Cheng, M., Luo, Y., Ouyang, J., Liu, Q., Liu, H., Li, L., Yu, S., Zhang, B., Cao, J., Ma, J., et al. (2025) · arXiv · doi:10.48550/arxiv.2503.10677 · Cited by 140
Metadata
- Authors
- Alghamdi, R., & Alfalqi, K.
- Venue
- International Journal of Advanced Computer Science and Applications
- Published
- 2015
- Volume
- 6
- Issue
- 1
- ISSN
- 2156-5570 (ISSN-L)
- 2158-107X
- OpenAlex
- W2132052677
- Cited by
- 377
- References
- 22
- FWCI
- 12.20
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
