Twitter Based Sentiment Analysis of GST Implementation by Indian Government

  • Prabhsimran SinghEmail author
  • Ravinder Singh Sawhney
  • Karanjeet Singh Kahlon
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 21)


Bringing major changes in existing tax structure is always a monotonous task to implement, especially when it affects one and all of the business world of one of the fastest growing economy. There are numerous hidden taxes, which remain inherently correlated with the goods reaching out to the general public. Implementation of Goods and Services Tax (GST) has been the biggest reform and a bold action performed by the Government of India recently. This paper takes into consideration the overall impact of GST implementation and the opinion of the Indian public about GST. Using our mathematically improvised modeling approach, we have done the sentiment analysis of the Twitter data collected over a period consisting of Pre-GST, In-GST and Post-GST period from all the regions and states of India. Multiple datasets are adopted to bring a rationalized outlook of this economic reform in Indian corporate scenario.


Goods and Services Tax Machine learning Twitter Sentiment analysis 


  1. 1.
  2. 2.
  3. 3.
  4. 4.
  5. 5.
    Salvador G, Luengo J, Francisco H (2015) Data preprocessing in data mining. Springer, New YorkGoogle Scholar
  6. 6.
    Singh P, Sawhney RS, Kahlon KS (2017) Forecasting the 2016 US presidential elections using sentiment analysis. In: Conference on e-Business, e-Services and e-Society. Springer, Cham, pp 412–423. Scholar
  7. 7.
    Maas AL, Daly RE, Pham TP, Haung, Ng AY, Potts C (2011) Learning word vectors for sentiment analysis. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, vol 1. Association for Computational Linguistics, pp 142–150Google Scholar
  8. 8.
    Singh P, Sawhney RS, Kahlon KS (2017) Sentiment analysis of demonetization of 500 and 1000 rupee banknotes by Indian government. ICT Express
  9. 9.
    Liu B (2012) Sentiment analysis and opinion mining. In: Synthesis lectures on human language technologies, vol 5, no 1, pp 1–167CrossRefGoogle Scholar
  10. 10.
    Witten IH, Frank E, Hall, MA Pal CJ (2016) Data mining: practical machine learning tools and techniques. Morgan KaufmannGoogle Scholar
  11. 11.
    Bernhard S, Christopher JCB, Alexander JS (1999) Advances in kernel methods: support vector learning. MIT PressGoogle Scholar
  12. 12.
    McCallum A, Nigam K (1998) A comparison of event models for Naive Bayes text classification. In: AAAI-98 workshop on learning for text categorization, vol 752, pp 41–48Google Scholar
  13. 13.
    Aha DW, Kibler D, Albert MK (1991) Instance-based learning algorithms. Mach Learn 6(1):37–66Google Scholar
  14. 14.
    Quinlan JR (1993) C4.5: programming for machine learning, vol 38. Morgan KauffmannGoogle Scholar
  15. 15.
    India Census 2011 Population Report. Accessed 10 Dec 2016

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Prabhsimran Singh
    • 1
    Email author
  • Ravinder Singh Sawhney
    • 2
  • Karanjeet Singh Kahlon
    • 1
  1. 1.Department of Computer ScienceGuru Nanak Dev UniversityAmritsarIndia
  2. 2.Department of Electronics TechnologyGuru Nanak Dev UniversityAmritsarIndia

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