A PRAGMATIC TEXT MINING ANALYSIS OF DEMONETISATION MOVE BASED ON TOPIC MODEL AND TWITTER COMMUNICATIONS

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Published: Feb 20, 2018
DOI: https://doi.org/10.26483/ijarcs.v9i1.5271
Keywords:
Demonetisation, Topic Model, Twitter, Text mining, Latent Dirichlet Allocation

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Doddi Srilatha
Sreenidhi Institute of Science and Technology
Shirisha Kakarla
Sreenidhi Institute of Science and Technology

Abstract

Demonetisation move has been the most trending topic on the twitter during the recent months. Indian government had taken a sudden decision to phase out the two most high valued denominations of Rupees 500 and 1000 currency notes. This decision was observed with different sentiments among the various sects of the people in the society. It caused sensation in the whole country. In this paper, we considered tweets related to demonetisation move and the speech contents that were delivered by the premier of the country. The significant number of tweets are downloaded, preprocessed to remove the noise like re-tweets, analyzed using frequency analysis and a topic model using Latent Dirichlet Allocation (LDA) is constructed. The LDA model automatically discovered the most relevant topics. Further clustering is done to project the clear view of the citizens’ and especially the public leaders’ opinions on this move. The most of the topics were related to criticize the Prime Minister’s speech addressed to the nation on new year event, although few opined the benefits of demonetisation.

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Vol. 9 No. 1 (2018): January-February 2018
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References

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