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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| Published in: | Information systems (Oxford) 2020-12, Vol.94, p.101582, Article 101582 |
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| Main Authors: | , |
| Format: | Article |
| Language: | eng |
| Subjects: | |
| Online Access: | Get full text |
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| Summary: | 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 |