Modeling Topic Evolution to Steer Interactive Information Search

In the modern context, as the data generated is exponential, finding meaningful patterns from large datasets is an urgent need. A ‘Topic Evolution Model’ could generate the evolutions related to a topic of user interest and assist in the exploration of patterns. In a generic setting, the proposed ‘topic evolution model’ assists researchers and domain experts for the relevant information extraction on scientific field progress and innovations in technological field or domain from large archives. The evolution patterns uncover the emerging, decay/fading, peculiar, and long-lasting research topics, and subtopics. The performance evaluation on coherence metrics asserts that the proposed model significantly minimizes the domain expert user efforts in topic analysis, as evolving patterns easily reveal underlying statistical and machine learning details. The perplexity metrics highlights the capability of the topic model towards the cognitive view of the user, i.e., change of ideas and knowledge through a period of time reducing the citation bias.