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

Abstract

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.

IRLCov19: A Large COVID-19 Multilingual Twitter Dataset of Indian Regional Languages

“…Identification of Location Information From Tweets The location information in a tweet can be identified in three different ways i.e by extracting the location information in the form of GPS coordinates from the downloaded JSON Twitter data, using the place or location information from the Tweet object [31][32] [33] and extracting the location information from the textual content [34]. The previous studies show that only 1% of the tweets contain GPS coordinates despite Twitter providing an option to capture the exact location of Tweet by enabling geolocation service on mobile devices.…”