A Survey of Topic Modeling in Text Mining

By Rubayyi Alghamdi, Khalid Alfalqi. 2015

355 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/a413c3f9414cce1adbf0205b540794191bbcfa8d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Apurva Shah
2. Tet Hin Yeap
3. Morad Benyoucef
4. Uttam Chauhan
5. Rania Albalawi
6. Roman Egger
7. Joanne Yu

In Information Systems

1. Apurva Shah
2. Morad Benyoucef
3. Uttam Chauhan

Citations in the corpus

• Topic Modeling Using Latent Dirichlet allocation (2021, background): “Alghamdi and Alfalqi surveyed the literature work in the light of two aspects, topic modeling methods and evolution of topics over time [2].”
• Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis (2020, background): “Alghamdi and Alfalqi (2015) proposed an empirical study of TM by categorizing the reviewed works into two popular approaches: topic evolution models and standard topic models with a time factor.”