A Survey of Topic Modeling in Text Mining 论文

摘要

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.