A social network analysis of opinions on GST in India within Twitter

Ganguly, Maitreyee; Roy, Sarbani

doi:10.1145/3170521.3170539

Cited by 3 publications

(1 citation statement)

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“…In [10] [11] a technique is used to process and analyse a huge amount of real time data using Hadoop cluster and Naive Bayes approach. The paper focuses on the speed of the sentiment analysis rather than accuracy of the analysis.…”

Section: Related Workmentioning

confidence: 99%

Citizens' Emotion on GST: A Spatio-Temporal Analysis over Twitter Data

Uniyal¹,

Rai²

2019

Preprint

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People might not be close-at-hand but they still are -by virtue of the social network. The social network has transformed lives in many ways. People can express their views, opinions and life experiences on various platforms be it Twitter, Facebook or any other medium there is. Such events constitute of reviewing a product or service, conveying views on political banters, predicting share prices or giving feedback on the government policies like Demonetization or GST. These social platforms can be used to investigate the insights of the emotional curve that the general public is generating. This kind of analysis can help make a product better, predict the future prospects and also to implement the public policies in a better way. Such kind of research on sentiment analysis is increasing rapidly. In this research paper, we have performed temporal analysis and spatial analysis on 1,42,508 and 58,613 tweets respectively and these tweets were posted during the post-GST implementation period from July 04, 2017 to July 25, 2017. The tweets were collected using the Twitter streaming API. A well-known lexicon, National Research Council Canada (NRC) emotion Lexicon is used for opinion mining that exhibits a blend of eight basic emotions i.e. joy, trust, anticipation, surprise, fear sadness, anger, disgust and two sentiments i.e. positive and negative for 6,554 words.

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Section: Related Workmentioning

confidence: 99%