##plugins.themes.bootstrap3.article.main##

The purpose of this study is to give an insight about Textual Data analytics and its application in the analysis of unique public relations campaign”Mann Ki Baat” that was initiated by incumbent Prime Minister of India,honourable “Mr.Narendra Modi” which was initially aired on All India Radio Programme on Vijaya Dashami on October 3rd , 2014 followed by second on November 2nd, 2014 of the same year till December 2019. In this paper, an analytical framework is designed using a powerful technique of textual data analytics “Topic Modelling based on LDA (Latent Dirichlet Allocation)” to accomplish the study. The proposed framework is applied to the corpus of 60 episodes(October 2014 to December 2019) of Mann ki Baat gathered from PMindia website and  was analyzed in greater detail. The terms used frequently and recurrence of the topics spoken in his popular monthly radio address  program were determined and analyzed from both in statistical and dynamic perspectives.In this context the present study is a first approach of application under the conventional technique “topic modelling” on Mann Ki Baat.Further, this is the principal endeavour to excerpt the themes discussed in radio programme using statistical modelling.

References

  1. Kumar A, Dabas V, Hooda P, Text classification algorithms for mining unstructured data: a SWOT analysis. Int. J. Inf. Technol. 1–11 (2018). https://doi.org/10.1007/s41870-017-0072-1.
     Google Scholar
  2. Tong Z., Zhang H.(2016). A Text Mining Research Based on LDA Topic Modelling.
     Google Scholar
  3. Gentzkow M, Kelly B, Taddy M(2019). Text as data. J. Econ. Lit.
     Google Scholar
  4. Mazarura J, Waal A De, Kanfer F, Millard S. Topic Modelling for Short Text. PrasaOrg 2014.
     Google Scholar
  5. Garg K. Sentiment analysis of Indian PM’s “Mann Ki Baat.” Int J Inf Technol [Internet]. Springer Science and Business Media LLC; 2020 [cited 2020 Feb 24];12:37–48. Available from: http://link.springer.com/10.1007/s41870-019-00324-8.
     Google Scholar
  6. Upadhyay S, Upadhyay N. Investigating Prime Minister Narendra Modi’s Usage of Pathos in the Cyber-Physical Society – A Case of Public Relations Campaign. Procedia Comput Sci. 2019.
     Google Scholar
  7. Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1999 (1999).
     Google Scholar
  8. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. (2003). https://doi.org/10.1016/b978-0-12-411519-4.00006-9.
     Google Scholar
  9. Dredze, M., Wallach, H.M., Puller, D., Pereira, F.: Generating summary keywords for emails using topics. In: International Conference on Intelligent User Interfaces, Proceedings IUI (2008).
     Google Scholar
  10. Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Natl. Acad. Sci. U. S. A. (2004). https://doi.org/10.1073/pnas.0307752101.
     Google Scholar
  11. Lau, J.H., Newman, D., Karimi, S., Baldwin, T.: Latent Dirichlet allocation. In: Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference (2010).
     Google Scholar
  12. Blei, D.M., Jordan, M.I.: Modeling Annotated Data. In: SIGIR Forum (ACM Special Interest Group on Information Retrieval) (2003).
     Google Scholar
  13. Rosen-Zvi, M., Chemudugunta, C., Griffiths, T., Smyth, P., Steyvers, M.: Learning author-topic models from text corpora. ACM Trans. Inf. Syst. (2010). https://doi.org/10.1145/1658377.1658381.
     Google Scholar
  14. McCallum, A., Corrada-Emmanuel, A., Wang, X.: Topic and role discovery in social networks. In: IJCAI International Joint Conference on Artificial Intelligence (2005).
     Google Scholar
  15. Griffiths, T.L., Steyvers, M., Blei, D.M., Tenenbaum, J.B.: Integrating topics and syntax. In: Advances in Neural Information Processing Systems (2005).
     Google Scholar
  16. Kulkarni, A., Shivananda, A.: Natural Language Processing Recipes. (2019).
     Google Scholar
  17. Gupta, M., Gupta, P.: Research and implementation of event extraction from twitter using LDA and scoring function. Int. J. Inf. Technol. 11, 365–371 (2019). https://doi.org/10.1007/s41870-018-0206-0.
     Google Scholar
  18. Anupriya, P., Karpagavalli, S.: LDA based topic modeling of journal abstracts. In: ICACCS 2015 - Proceedings of the 2nd International Conference on Advanced Computing and Communication Systems (2015).
     Google Scholar