Concept Embedded Topic Modeling Technique

Text contents are overloaded with the digitization of the data and new contents are transmitted through many sources by generating a large volume of information, which spreads all over the world through different communication media. Therefore, text data is available everywhere and reading, understanding and analysing the text data has become a main activity in daily routine. With the increment of the volume and the variety of information, organizing and searching, the required information has become vital. Topic modelling is the state of the art for information organization, understanding and extracting the content. Most of the prevailing topic models use the probabilistic approaches and consider the frequency and the co-occurrence to discover the topics from collections of documents. The proposed research aims to address the existing problems of topic modeling by introducing a concept embedded topic model which generates the most relevant and meaningful topics by understanding the content. The research includes approaches to understand the semantic elements from the content, domain identification of concepts and provide most suitable topics without getting the number of topics from the user beforehand. Capturing the semantics of document collections and generating the most related set of topics according to the actual meaning will be the significance of this research.