Topic Modeling: Exploring the Processes, Tools, Challenges andApplications

Topic modeling is a data analysis technique that has become increasingly popular in recentyears due to the growing availability of large datasets. It enables researchers to uncoverunderlying themes and topics within large and complex datasets by identifying patternsand relationships among data points. This article provides an overview of the topicmodeling process, including the algorithms used, tools,evaluation metrics, and applicationsacross various fields. It also discusses potential areas for future research, such as theintegration of other machine learning techniques and the incorporation of temporal andcontextual information. Overall, topic modeling represents a powerful tool for data analysisthat has the potential to unlock new insights and understanding in a variety of domains

کلیدواژه ها:

Topic Modeling ، NaturalLanguage Processing ، LatentDirichlet Allocation ، NonnegativeMatrix Factorization ، Latent Semantic Analysis