Quick Insight of Research Literature Using Topic Modeling

  • Vrishali ChakkarwarEmail author
  • Sharvari C. Tamane
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 165)


With the development in Information technology and advancement in education and research, a huge number of research publications and articles are generated every year. Crucial knowledge and information about innovative technology are embedded in these documents. It has become necessary to find a text analytics technique that gives quick insight into the research content from such enormous unstructured text data. Here we proposed information retrieval technique using topic modeling for taking quick outlook of the data. Latent Dirichlet is a generative probabilistic model which uses probability distribution of words in document to extract theme of documents. Topics generated show many hidden themes, correlated terminologies to main theme of documents which gives a quick overview of these documents. In this work Blockchain Technology, emergent technology publications are considered.


Topic modeling Latent Dirichlet Allocation Text analytics 



We like to acknowledge the Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India for supporting Research Facilities. We also like to Thank Head, Computer Science and Engineering Department and Principal, Govt. College of Engineering, Aurangabad, Maharashtra, India for their valuable guidance in different aspect of this paper.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Government College of EngineeringAurangabadIndia
  2. 2.Jawaharlal Nehru Engineering CollegeAurangabadIndia

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