Abstract
Topic models provide a convenient way to analyze large of unclassified text. A topic contains a cluster of words that frequently occur together. A topic modeling can connect words with similar meanings and distinguish between uses of words with multiple meanings. This paper provides two categories that can be under the field of topic modeling. First one discusses the area of methods of topic modeling, which has four methods that can be considerable under this category. These methods are Latent semantic analysis (LSA), Probabilistic latent semantic analysis (PLSA), Latent Dirichlet allocation (LDA), and Correlated topic model (CTM). The second category is called topic evolution models, which model topics by considering an important factor time. In the second category, different models are discussed, such as topic over time (TOT), dynamic topic models (DTM), multiscale topic tomography, dynamic topic correlation detection, detecting topic evolution in scientific literature, etc.
Highlights
To have a better way of managing the explosion of electronic document archives these days, it requires using new techniques or tools that deals with automatically organizing, searching, indexing, and browsing large collections
Probabilistic Latent Semantic Analysis (PLSA) is an approach that has been released after LSA method to fix some disadvantages that have found into LSA
Correlated Topic Model (CTM) is a kind of statistical model used in natural language processing and machine learning
Summary
Information Systems Security CIISE, Concordia University Montreal, Quebec, Canada. Abstract—Topic Modeling provides a convenient way to analyze big unclassified text. This paper provides two categories that can be considered under the field of topic modeling. First one discusses the area of methods of Topic Modeling, which has four methods and can be considered under this category. These methods are Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocation (LDA), and Correlated Topic Model (CTM). The second category is called Topic Evolution Model, it considers an important factor time. In this category, different models are discussed, such as Topic Over Time (TOT), Dynamic Topic Models (DTM), Multiscale Topic Tomography, Dynamic Topic Correlation Detection, Detecting Topic Evolution in scientific literatures, etc
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
