In information retrieval, topic modeling is used to predict hidden subjects from a text corpus. As a result, it offers a system for organizing, comprehending, and summarising vast amounts of text data automatically. Topic modeling can also be used to provide a document representation that may be interpreted in a broad spectrum of natural-language processing (NLP) applications Techniques for modeling span from probabilistic graphs models to neural models. This research looks at subject models from a variety of angles. The first element categorizes subject modeling strategies as algebraic, fuzzy, probabilistic, or neural. We investigate enormous number of handy models in every group, emphasize contrasts and similarities among models and model types from a unifying standpoint, examine the properties and limits of these models, and debate the correctness of the application. Another factor to consider is a review of datasets and benchmarks. We talk about studies done on datasets to evaluate topic models along stated measures. The study focuses on the contrast between both models and their rightness for miscellaneous applications. |
