• Srinivas S
• Vishnu priya K
• G. Ragu

Over the last years topic modeling has emerged as a powerful technique for organizing and summarizing big collections of documents or searching for patterns in them. However, privacy concerns arise when cross-analyzing data from different sources is required. Federated topic modeling solves this issue by allowing multiple parties to jointly train a topic model without sharing their data. Topic models simultaneously model the latent topic structure of large collections of documents and a response variable associated with each document. It is popular to detect hot topics which can benefit many tasks including topic recommendations the guidance of public opinions and so on. However, in some cases people may want to know when to re-hot a topic i.e. make the topic popular again. Also, it considers the continuous temporal modeling of topics since topics are changing continuously over time. Furthermore, a weighting scheme is proposed to smooth the fluctuations in topic re-hotting prediction. In this proposed system we explore task-centric topic model comparison considering how we can both provide detail for a more nuanced understanding of differences and address the wealth of tasks for which topic models are used. We derive comparison tasks from single-model uses of topic models which predominantly fall into the categories of understanding topics understanding similarity and understanding change. We abstract the model complexity in an interactive visual workspace for exploring the automatic matching results of two models investigating topic summaries analyzing parameter distributions and reviewing documents. The main contribution of our work is an iterative decision-making technique in which users provide document-based relevance feedback that allows the framework to converge to a user-endorsed topic distribution.

"Enhancement of Topics Modeling using Probabilistic Latent Semantic Analysis Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.m230-m233, April-2023, Available :http://www.jetir.org/papers/JETIR2304C26.pdf

"Enhancement of Topics Modeling using Probabilistic Latent Semantic Analysis Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppm230-m233, April-2023, Available at : http://www.jetir.org/papers/JETIR2304C26.pdf