A topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for the discovery of hidden semantic structures in a text body.
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
The proposed method trains the neural networks such that the expected test likelihood is improved when topic model parameters are estimated by maximizing the posterior probability using the priors based on the EM algorithm.
For web pages nested inside web sites, local topic models explicitly label local topics and identifies the owning web site.
Network-based procedures for topic detection in huge text collections offer an intuitive alternative to probabilistic topic models.
Social media such as Twitter provide valuable information to crisis managers and affected people during natural disasters.
Topic modelling has been a successful technique for text analysis for almost twenty years.
In this paper, we propose a Renyi entropy-based approach for a partial solution to the above problem.
Topic models are widely used analysis techniques for clustering documents and surfacing thematic elements of text corpora.