Determining the popularity of political parties using twitter sentiment analysis

Sujeet Sharma, Nisha P. Shetty

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationInformation and Decision Sciences - Proceedings of the 6th International Conference on FICTA
PublisherSpringer Verlag
Pages21-29
Number of pages9
ISBN (Print)9789811075629
DOIs
Publication statusPublished - 01-01-2018
Externally publishedYes
Event6th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2017 - Bhubaneswar, India
Duration: 14-10-201715-10-2017

Publication series

NameAdvances in Intelligent Systems and Computing
Volume701
ISSN (Print)2194-5357

Conference

Conference6th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2017
CountryIndia
CityBhubaneswar
Period14-10-1715-10-17

Fingerprint

Websites
Support vector machines
Classifiers
Internet

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Sharma, S., & Shetty, N. P. (2018). Determining the popularity of political parties using twitter sentiment analysis. In Information and Decision Sciences - Proceedings of the 6th International Conference on FICTA (pp. 21-29). (Advances in Intelligent Systems and Computing; Vol. 701). Springer Verlag. https://doi.org/10.1007/978-981-10-7563-6_3
Sharma, Sujeet ; Shetty, Nisha P. / Determining the popularity of political parties using twitter sentiment analysis. Information and Decision Sciences - Proceedings of the 6th International Conference on FICTA. Springer Verlag, 2018. pp. 21-29 (Advances in Intelligent Systems and Computing).
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Sharma, S & Shetty, NP 2018, Determining the popularity of political parties using twitter sentiment analysis. in Information and Decision Sciences - Proceedings of the 6th International Conference on FICTA. Advances in Intelligent Systems and Computing, vol. 701, Springer Verlag, pp. 21-29, 6th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2017, Bhubaneswar, India, 14-10-17. https://doi.org/10.1007/978-981-10-7563-6_3

Determining the popularity of political parties using twitter sentiment analysis. / Sharma, Sujeet; Shetty, Nisha P.

Information and Decision Sciences - Proceedings of the 6th International Conference on FICTA. Springer Verlag, 2018. p. 21-29 (Advances in Intelligent Systems and Computing; Vol. 701).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Sharma S, Shetty NP. Determining the popularity of political parties using twitter sentiment analysis. In Information and Decision Sciences - Proceedings of the 6th International Conference on FICTA. Springer Verlag. 2018. p. 21-29. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-981-10-7563-6_3