A review of topic modeling methods

Topic modeling is a popular analytical tool for evaluating data. Numerous methods of topic modeling have been developed which consider many kinds of relationships and restrictions within datasets; however, these methods are not frequently employed. Instead many researchers gravitate to Latent Dirich...

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Veröffentlicht in:Information systems (Oxford) Jg. 94; S. 101582
Hauptverfasser: Vayansky, Ike, Kumar, Sathish A.P.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Elsevier Ltd 01.12.2020
Elsevier Science Ltd
Schlagworte:
Algorithms
Data
Data mining
Digital media
Dirichlet problem
Inference algorithms
Information systems
Modelling
Probabilistic Bayesian networks
Social media
Social Media analysis
Temporal analysis
Text analysis
Topic correlation
Topic modeling
Topics
Topic modeling
Temporal analysis
Text analysis
Probabilistic Bayesian networks
Social Media analysis
Topic correlation
Inference algorithms
ISSN:0306-4379, 1873-6076
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Zusammenfassung:Topic modeling is a popular analytical tool for evaluating data. Numerous methods of topic modeling have been developed which consider many kinds of relationships and restrictions within datasets; however, these methods are not frequently employed. Instead many researchers gravitate to Latent Dirichlet Analysis, which although flexible and adaptive, is not always suited for modeling more complex data relationships. We present different topic modeling approaches capable of dealing with correlation between topics, the changes of topics over time, as well as the ability to handle short texts such as encountered in social media or sparse text data. We also briefly review the algorithms which are used to optimize and infer parameters in topic modeling, which is essential to producing meaningful results regardless of method. We believe this review will encourage more diversity when performing topic modeling and help determine what topic modeling method best suits the user needs. •Reviewed different topic modeling approaches dealing with correlation between topics.•This review will encourage more diversity when performing topic modeling.•The classification of methods in our review is flexible.•Discussed the techniques of optimizing the topic modeling algorithms.•Created and presented a decision tree to select a topic modeling method
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ISSN:0306-4379
1873-6076
DOI:10.1016/j.is.2020.101582