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DOI: 10.14569/IJACSA.2015.060121

A Survey of Topic Modeling in Text Mining

Author 1: Rubayyi Alghamdi
Author 2: Khalid Alfalqi

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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 6 Issue 1, 2015.

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Abstract: 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.

Keywords: Topic Modeling; Methods of Topic Modeling; Latent semantic analysis (LSA); Probabilistic latent semantic analysis (PLSA); Latent Dirichlet allocation (LDA); Correlated topic model (CTM); Topic Evolution Modelin

Rubayyi Alghamdi and Khalid Alfalqi, “A Survey of Topic Modeling in Text Mining” International Journal of Advanced Computer Science and Applications(IJACSA), 6(1), 2015. http://dx.doi.org/10.14569/IJACSA.2015.060121

@article{Alghamdi2015,
title = {A Survey of Topic Modeling in Text Mining},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2015.060121},
url = {http://dx.doi.org/10.14569/IJACSA.2015.060121},
year = {2015},
publisher = {The Science and Information Organization},
volume = {6},
number = {1},
author = {Rubayyi Alghamdi and Khalid Alfalqi}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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