• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology

Article

Indian Journal of Science and Technology

Year: 2022, Volume: 15, Issue: 37, Pages: 1859-1867

Original Article

Textual Data Analysis of ‘Mann Ki Baat’ Show

Received Date:20 April 2022, Accepted Date:18 August 2022, Published Date:26 September 2022

Abstract

Background: The present research gives insights about Textual Data analytics of “Mann Ki Baat” – a monthly radio programme hosted by Shri. Narendra Modi, the Prime Minister of India on All India Radio. Using techniques of textual data analytics, an analytical framework is designed in this research paper. Methods: The outline is applied to the corpus of twelve (12) episodes of Mann Ki Baat 2.0 (March 2020 – February 2021, after nationwide a lockdown in India) collected from PMIndia’s official website and was studied in detail. Recurrently spoken words, diversity of topics covered, Topics Correlations, Sentiment Analysis, and Hierarchical Clustering were determined and analyzed. Further, word clouds for each episode were also generated. Findings: The research is a new approach of an application under the traditional “topic modeling” techniques on Mann Ki Baat 2.0. Novelty and applications: Additionally, this research is an attempt to extract the themes using statistical modeling deliberated in the popular radio programme – Mann Ki Baat 2.0.

Keywords: Textual Data Analytics Topics Correlations; Sentiment Analysis; Hierarchical Clustering; Word Clouds; Mann Ki Baat 2.0

References

  1. Hofmann T. Probabilistic latent semantic indexing. Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '99. 1999.
  2. Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. J. Mach. Learn. Res. 2003. Available from: https://doi.org/10.1016/b978-0-12-411519-4.00006-9
  3. Dredze M, Wallach HM, Puller D, Pereira F. Generating summary keywords for emails using topics. Proceedings of the 13th international conference on Intelligent user interfaces - IUI '08. 2008.
  4. Griffiths TL, Steyvers M, Blei DM, Tenenbaum JB. Integrating topics and syntax. Advances in Neural Information Processing Systems. 2005.
  5. Griffiths TL, Steyvers M. Finding scientific topics. Proceedings of the National Academy of Sciences. 2004;101(suppl_1):5228–5235. Available from: https://doi.org/10.1073/pnas.0307752101
  6. Lau JH, Newman D, Karimi S, Baldwin T. Latent Dirichlet allocation. Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference. 2010.
  7. Merriam-Webster. Sentiment. Available from: https://www.merriam-webster.com/dictionary/sentiment (accessed )
  8. Yi J, Nasukawa T, Bunescu R, Niblack W. Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques. Third IEEE International Conference on Data Mining. 2003;p. 427–434.

Copyright

© 2022 Makwana & Ganatra. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published By Indian Society for Education and Environment (iSee

DON'T MISS OUT!

Subscribe now for latest articles and news.