Topic Modelling Extraction of “Mann Ki Baat”



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Submitted Dec 31, 2020
Published Jan 27, 2021
10.24018/ejmath.2021.2.1.11

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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.

Keywords: Document Term Matrix (DTM) Latent Dirichlet Allocation (LDA) Mann Ki Baat (MKB) Textual Data Analytics Textual Mining Topic Modelling

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