Determining the Popularity of Political Parties Using Twitter Sentiment Analysis

  • Sujeet Sharma
  • Nisha P. Shetty
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 701)


With the advancement in the Internet Technology, many people have started connecting to social networking websites and are using these microblogging websites to publically share their views on various issues such as politics, celebrity, or services like e-commerce. Twitter is one of those very popular microblogging website having 328 million of users around the world who posts 500 million of tweets per day to share their views. These tweets are rich source of opinionated User-Generated Content (UGC) that can be used for effective studies and can produce beneficial results. In this research, we have done Sentiment Analysis (SA) or Opinion Mining (OM) on user-generated tweets to get the reviews about major political parties and then used three algorithms, Support Vector Machine (SVM), Naïve Bayes Classifier, and k-Nearest Neighbor (k-NN), to determine the polarity of the tweet as positive, neutral, or negative, and finally based on these polarities we made a prediction of which party is likely to perform more better in the upcoming election.


Sentiment analysis Opinion mining Tokenization Classification Natural language processing (NLP) 


  1. 1.
    Yadav, S.K.: Sentiment analysis and classification: a survey. Int. J. Advanc. Res. Comput. Sci. Manag. Studies, 3(3) (2015)Google Scholar
  2. 2.
    Taimur, I., Ataur, R.B., Tanzila, R., Mohammad, S.U.: Filtering political sentiment in social media from textual information. In: 5th International Conference on Informatics, Electronics and Vision (2016)Google Scholar
  3. 3.
    Alexander, P., Patrick, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: International Conference on Language Resources and Evaluation (2010)Google Scholar
  4. 4.
    Akhil Kumar, K.V., Manikanth Sai, G.V., Shetty, N.P., Chetana, P., Aishwarya, B.: Aspect based sentiment analysis using R programming. In: Proceedings of Fourth International Conference on Emerging Research in Computing, Information, Communication and Applications (ERCICA-2016)Google Scholar
  5. 5.
    Shengyi, J., Guansong, P., Meiling, W., Limin, K.: An improved K-nearest-neighbour algorithm for text categorization. Exp. Syst. Appl. 391(3) (2012)Google Scholar
  6. 6.
    Karim, M., Rahman, R.M.: Decision tree and Naïve Bayes algorithm for classification and generation of actionable knowledge for direct marketing. J. Softw. Eng. Appl. 6, 196–206 (2013)CrossRefGoogle Scholar
  7. 7.
    Hsu, C.W., Chang, C.C., Lin, C.J.: A Practical Guide to Support Vector
  8. 8.
    Kousar Nikhath, A., Subrahmanyam, K., Vasavi, R.: Building a K-nearest neighbour classifier for text categorization. Int. J. Comput. Sci. Informat. Technol. 7(1), 254–256 (2016)Google Scholar
  9. 9.
    Alexandra, B., Marco, T.: Improving sentiment analysis in twitter using multilingual machine translated data. In: Recent Advances in Natural Language Processing (2013)Google Scholar
  10. 10.
    Dilara, T., Gurkan, T., Ozgun Sagturk, Ganiz, M.C.: Wikipedia based semantic smoothing for Twitter sentiment classification. IEEE (2013)Google Scholar
  11. 11.
    Minqing, H., Bing, L.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’04 pp. 168–177, ACM New York (2004)Google Scholar
  12. 12.
    Chien-Liang, L., Wen-Hoar, H., Chia-Hoang, L., Gen-Chi, L., Emery, J.: Movie rating and review summarization in mobile environment. IEEE Trans. Syst. Man Cybernet. Part C Appl. Rev. 42 (2012)Google Scholar
  13. 13.
  14. 14.
    Pujari, C., Aiswarya, Shetty, N.P.: Comparison of classification techniques for feature oriented sentiment analysis of product review data. In: Data Engineering and Intelligent Computing, pp 149–158 (2017)Google Scholar
  15. 15.
    Vladimir, N.V.: The Nature of Statistical Learning Theory. Springer, New York (1995)Google Scholar
  16. 16.
  17. 17.
    David, M.: Support Vector Machines: The Interface to libsvm in package e1071 (2017)Google Scholar
  18. 18.

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Sikkim Manipal Institute of TechnologyEast SikkimIndia
  2. 2.Manipal Institute of Technology, Manipal UniversityManipalIndia

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