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Sentiment Analysis and Prediction of Election Results 2018

  • Urvashi SharmaEmail author
  • Rattan K. Datta
  • Kavita Pabreja
Conference paper
  • 14 Downloads
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 100)

Abstract

Social media is becoming very popular throughout the planet. People feel very comfortable in expressing their views freely. It was decided to take the help from this fact for sentiment analysis of the discussion and their free comments with regard to the forthcoming assembly elections in India. The social media data is analyzed by applying different mining techniques to predict the possible outcomes of the assembly elections and watching the positions of various political parties in India. An attempt has been made to analyze the user behavior on social media in order to find out the prediction of political parties’ position in election.

Keywords

Sentiment analysis Social media Big data analytics R language Data mining Election prediction 

References

  1. 1.
    Chamatkar AJ, Butey PK (2014) Importance of data mining with different types of data applications and challenging areas. Int J Eng Res Appl 4(5) (Version 3):38–41. ISSN: 2248-9622, www.ijera.com
  2. 2.
    Verma JP, Agrawal S, Patel B, Patel A (2016) Big data analytics: challenges and applications for text, audio, video, and social media data. Int J Soft Comput Artif Intell Appl (IJSCAI) 5(1)CrossRefGoogle Scholar
  3. 3.
    Ali A, Qadir J, Rasool R, Sathiaseelan A, Zwitter A, Crowcroft J (2016) Big data for development: applications and techniques. Big Data Anal 1:2.  https://doi.org/10.1186/s41044-016-0002-4CrossRefGoogle Scholar
  4. 4.
    Vedanayaki M (2014) A study of data mining and social network analysis. Ind J Sci Technol 7(S7):185–187Google Scholar
  5. 5.
    Fernando SGS, Perera SN (2014) Empirical analysis of data mining techniques for social network websites. Compusoft 3(2):582Google Scholar
  6. 6.
    Nandi G, Das A (2013) A survey on using data mining techniques for online social network analysis. Int J Comput Sci (IJCSI) 10(6):162. ISSN (Print): 1694-0814, ISSN (Online): 1694-0784, www.IJCSI.org
  7. 7.
    Kaur R, Singh S (2016) A survey of data mining and social network analysis based anomaly detection techniques. Egypt Inf J 17(2):199–216CrossRefGoogle Scholar
  8. 8.
    Kumari S (2016) Impact of big data and social media on society. Glob J Res Anal 5(3). ISSN No. 2277-8160Google Scholar
  9. 9.
    Asur S, Huberman BA (2010) Predicting the future with social media. arXiv:1003.5699v1 [cs.CY], 29 Mar 2010
  10. 10.
    Maia M, Almeida J, Almeida V (2008) Identifying user behavior in online social networks. Proceedings of the 1st workshop on social network systems. ACM, pp 1–6Google Scholar
  11. 11.
    Vinerean S, Cetina I, Dumitrescu L, Tichindelean M (2013) The effects of social media marketing on online consumer behavior. Int J Bus Manage 8(14). ISSN 1833-3850. E-ISSN 1833-8119. Published by Canadian Center of Science and EducationGoogle Scholar
  12. 12.
    Benevenutoy F, Rodriguesy T, Cha M, Almeiday V (2009) Characterizing user behavior in online social networks. Proceedings of the 9th ACM SIGCOMM conference on Internet measurement. ACM, 2009Google Scholar
  13. 13.
    Jiang J, Wilson C, Wang X, Huang P, Sha W, Dai Y, Zhao BY (2010) Understanding latent interactions in online social networks. In: IMC’10, Melbourne, Australia, 1–3 Nov 2010Google Scholar
  14. 14.
    Pippal S, Batra L, Krishna A, Gupta H, Arora K (2014) Data mining in social networking sites: a social media mining approach to generate effective business strategies. Int J Innov Adv Comput Sci (IJIACS) 3(2):22–27. ISSN 2347-8616Google Scholar
  15. 15.
    Chen H, Chung W, Xu JJ, Wang G, Qin Y, Chau M (2004) Crime data mining: a general framework and some examples. Computer 4:50–56CrossRefGoogle Scholar
  16. 16.
    Umar KI, Chiroma F (2016) Data mining for social media analysis: using twitter to predict the 2016 US presidential election. Int J Sci Eng Res 7(10):1972–1980. ISSN 2229-5518Google Scholar
  17. 17.
    Choy M, Cheong MLF, Laik MN, Shung KPChoy M, Cheong MLF, Laik MN, Shung KP (2011) A sentiment analysis of Singapore presidential election 2011 using Twitter data with census correction. IJRET Int J Res Eng Technol, eISSN: 2319-1163, pISSN: 2321-7308Google Scholar
  18. 18.
    Narwal N, Pabreja K (2018) Social media analytics. CSI Commun 42(5&6):10–13Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Urvashi Sharma
    • 1
    Email author
  • Rattan K. Datta
    • 2
  • Kavita Pabreja
    • 3
  1. 1.IPS AcademyIndoreIndia
  2. 2.Mohyal Educational and Research Institute of TechnologyNew DelhiIndia
  3. 3.Maharaja Surajmal Institute, GGSIPUNew DelhiIndia

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