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Analyzing and Preprocessing the Twitter Data for Opinion Mining

  • Neetu AnandEmail author
  • Dhruvi Goyal
  • Tapas Kumar
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 34)

Abstract

With a rapid growth of the world of Internet, the social media is eventually growing and is playing a very major role in most of our lives. There are various social networking sites such as Twitter, Google+, Facebook which provide a platform for the people to present themselves. Twitter is an efficient micro-blogging tool which has become very popular throughout the world. Nowadays, there is an ongoing trend of posting every thought and emotion of one’s life on these social networking sites. Due to this, emotion analysis has gained popularity in analyzing the thoughts, opinions, feelings, sentiments, etc., of various people. The present paper is based on the demonstration of a complete step-by-step process of analyzing emotions from tweets related to Budget 2017.

Keywords

Twitter Data analysis Sentiments Social media Emotion analysis Budget 2017 

References

  1. 1.
    Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of twitter data. In: Proceedings of the Workshop on Languages in Social Media, LSM’11, pp. 30–38, Stroudsburg, PA, USA (2011)Google Scholar
  2. 2.
    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, HLT’05, pp. 347–354, Stroudsburg, PA, USA (2005)Google Scholar
  3. 3.
    Liang, M., Trejo, C., Muthu, L., Ngo, L., B., Luckow, A., Amy, W.: Evaluating R-based big data analytic frameworks. In: IEEE International Conference on Cluster Computing, pp. 508–509 (2015)Google Scholar
  4. 4.
    Rosenthal, S., Nakov, P., Kiritchenko, S., Mohammad, S., Ritter, A., Stoyanov, V.: Semeval-2015 task 10: sentiment analysis in twitter. In: Proceedings of the 9th national Workshop on Semantic Evaluation, SemEval ’2015, Denver, Colorado (2015)Google Scholar
  5. 5.
    Pang, B., Lee, L., Vaithyanathan, S.: Sentiment classification using machine learning techniques. In: Proceedings of ACL, pp. 79–86 (2002)Google Scholar
  6. 6.
    O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: linking text sentiment to public opinion time series. In Proceedings of ICWSM (2010)Google Scholar
  7. 7.
    Parikh, R., Movassate M.: Sentiment analysis of user-generated twitter updates using various classification techniques. CS224 N Final Report (2009)Google Scholar
  8. 8.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Stanford University, Technical Paper (2009)Google Scholar
  9. 9.
    Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of the Seventh Conference on International Language Resources and Evaluation, pp. 1320–1326 (2010)Google Scholar
  10. 10.
    Bifet, A., Frank, E.: Sentiment knowledge discovery in twitter streaming data. In: Proceedings of the 13th International Conference on Discovery Science, Springer, Berlin, Germany, pp. 1–15 (2010)Google Scholar
  11. 11.
    Tiwari, V., Thakur, R.S.: Contextual snowflake modelling for pattern warehouse logical design. Sadhana—Academy Proceedings in Engineering Science, vol. 40, pp. 15–33, Springer (2015)Google Scholar
  12. 12.
    Jahiruddin: Sentiment analysis of twitter data using statistical methods. In: International Journal of Innovative Research in Engineering & Management (IJIREM) (2015)Google Scholar
  13. 13.
    Anber, H., Salah, A., El-Aziz A.A. Abd.: A literature review on twitter data analysis. In: International Journal of Computer and Electrical Engineering (2016)Google Scholar
  14. 14.
    Hemalatha, I., Varma, G.P.S., Govardhan, A.: Preprocessing the informal text for efficient sentiment analysis. IJETTCS (2012)Google Scholar
  15. 15.
  16. 16.
  17. 17.
  18. 18.
  19. 19.
  20. 20.
  21. 21.
    Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau R.: Sentiment analysis of twitter data. In: Proceedings of the ACL Workshop on Languages in Social Media, pp. 30–38 (2011)Google Scholar
  22. 22.
    Liang, P.-W., Dai B.-R.: Opinion mining on social media data. In: IEEE International Conference on Mobile Data Management, Milan, Italy, June 3–6, 2013, pp. 91–96, ISBN: 978-1-494673-6068-5, http://doi.ieeecomputersociety.org/10.1109/MDM (2013)
  23. 23.
    Kharde, Vishal A., Sonawane. S.S.: Sentiment analysis of twitter data—a survey of techniques. In: International Journal of Computer Applications (0975-8887), vol. 139, no. 11 (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Maharaja Surajmal InstituteNew DelhiIndia
  2. 2.Lingayas UniversityFaridabadIndia

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