Abstract
Topic modeling is the task of identifying topics in a corpus of documents. This is useful for search engines, customer service automation, and any other situation where document topics are important to know. There are numerous studies conducted about this field. Given the importance of topic modeling in various areas, this survey paper, explore some articles related to topic modeling (between 2013 to 2020) based on multiple methods such as: Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), and Non Negative Matrix Factorization (NMF), on different social media sources such as (Amazon, Reddit, and Yelp). LDA and NMF general concepts are presented, in addition to the challenges of topic modeling and methods of evaluation. This paper does not go deep into the details of each of these methods. It only describes the high-level view that related to topic modeling in text mining.
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