Abstract
This paper represents an experimental approach for sentiment analysis of GST tweets, along with generating the most popular GST buzzwords with their respective popularity to polarity scores within a given range of tweets. In India, one of the most trending issues of 2017 was the implementation of Goods and Services Tax (GST) during June–July 2017. GST is a single tax-settled nation wise upon the supply chain of goods and services, primitively from the manufacturer to the consumer. GST was launched in India on the midnight of June 30, 2017, marked by a midnight (June 30–July 1, 2017) session of both the houses of parliament coalesced at the Central Hall of the Parliament. This new taxation system is governed by an GST Council formed by the Govt. of India and the chairman of this council is the Finance Minister of India, himself. As an immediate effect of the cumulation of these ongoing events, a lot of opinion contrast emerged on popular social networks such as Twitter, Facebook regarding this new taxation system. Inspired from this entire event, we propose a new Twitter-based polarity–popularity framework and words cluster for identifying most popular GST-exclusive word counts and sentiment analysis within a given range of tweets, which can be the good indicators of words occurrence probability specific to such an event regarding this topic. This work gives us a much more detailed aspect of the lexical-level analysis of tweets from several directions, along with the future improvement prospects.
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Das, S., Das, D., Kolya, A.K. (2020). An Approach for Sentiment Analysis of GST Tweets Using Words Popularity Versus Polarity Generation. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_7
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