Research Area: Machine Learning
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.
Keywords:
Topic modeling
Probabilistic Bayesian networks
Text analysis
Topic correlation
Temporal analysis
Social Media analysis
Inference algorithms
Author(s) Name: Ike Vayansky, Sathish A.P. Kumar
Journal name: Information Systems
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.is.2020.101582
Volume Information: Volume 94, December 2020, 101582
