Sentiment Score Analysis for Opinion Mining

  • Nidhi SinghEmail author
  • Nonita Sharma
  • Akanksha Juneja
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)


Sentiment Analysis has been widely used as a powerful tool in the era of predictive mining. However, combining sentiment analysis with social network analytics enhances the predictability power of the same. This research work attempts to provide the mining of the sentiments extracted from Twitter Social App for analysis of the current trending topic in India, i.e., Goods and Services Tax (GST) and its impact on different sectors of Indian economy. This work is carried out to gain a bigger perspective of the current sentiment based on the live reactions and opinions of the people instead of smaller, restricted polls typically done by media corporations. A variety of classifiers are implemented to get the best possible accuracy on the dataset. A novel method is proposed to analyze the sentiment of the tweets and its impact on various sectors. Further the sector trend is also analyzed through the stock market analyses and the mapping between the two is made. Furthermore, the accuracy of stated approach is compared with state of art classifiers like SVM, Naïve Bayes, and Random forest and the results demonstrate accuracy of stated approach outperformed all the other three techniques. Also, a detailed analysis is presented in this manuscript regarding the effect of GST along with time series analysis followed by gender-wise analysis.


Sentiment analysis Goods and services tax Classification Text mining Support vector machine Naïve bayes classifier Random forest 


  1. 1.
    Dani, S.: A research paper on an impact of goods and service tax (GST) on Indian Economy. Bus Eco J 7, 264 (2016). Scholar
  2. 2.
    Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)CrossRefGoogle Scholar
  3. 3.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing. Association for Computational Linguistics (2005)Google Scholar
  4. 4.
    Alessia, D., et al.: Approaches, tools and applications for sentiment analysis implementation. Int. J. Comput. Appl. 125, 3 (2015)Google Scholar
  5. 5.
    Bickart, B., Schindler, R.M.: Internet forums as influential sources of consumer information. J. Interact. Market. 15(3), 31–40 (2001). ISSN 1094-9968.
  6. 6.
    Yu, Y., Duan, W., Cao, Q.: The impact of social and conventional media on firm equity value: a sentiment analysis approach. Decis. Support Syst. 55(4), 919–926 (2013). ISSN 0167-9236.
  7. 7.
    Prabowo, R., Thelwall, M.: Sentiment analysis: a combined approach. J. Informetr. 3(2), 143–157 (2009)CrossRefGoogle Scholar
  8. 8.
    Hiroshi, K., Tetsuya, N., Hideo, W.: Deeper sentiment analysis using machine translation technology. In: Proceedings of the 20th International Conference on Computational Linguistics (COLING 2004), Geneva, Switzerland, 23–27 Aug 2004, pp. 494–500Google Scholar
  9. 9.
    Trending Hashtags. Accessed 15 Sept 2017

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.National Institute of Technology DelhiNew DelhiIndia
  2. 2.Dr. B. R. Ambedkar National Institute of Technology JalandharJalandharIndia
  3. 3.Jawaharlal Nehru UniversityNew DelhiIndia

Personalised recommendations