Sentiment Analysis of Twitter Data Using Big Data Tools and Hadoop Ecosystem

  • Monica Malik
  • Sameena NaazEmail author
  • Iffat Rehman Ansari
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


Sentiment analysis and opinion mining help in the analysis of people’s views, opinions, attitudes, emotions and sentiments. In this twenty-first century, huge amount of opinionated data recorded in the digital form is available for analysis. The demand of sentiment analysis occupies the same space with the growth of social media such as Twitter, Facebook, Quora, blogs, microblogs, Instagram and other social networks. In this research work, the most popular microblogging site ‘twitter’ has been used for sentiment analysis. People’s views, opinions, attitudes, emotions and sentiments on an outdoor game ‘Lawn Tennis’ have been used for the analysis. This is done by analysing people’s positive, neutral and negative reviews posted on Twitter. Through this it has been analysed that how many people around the world really like this game and how popular this game is in different countries.


Big data Data mining Twitter Sentiment analysis Lawn tennis 


  1. 1.
    Ingle A, Kante A, Samak S, Kumari A (2015) Sentiment analysis of twitter data using hadoop. Int J Eng Res Gen Sci 3(6)Google Scholar
  2. 2.
    Pang B, Lee L (2008) Opinion mining and sentiment analysis. Now Publishers Inc., Foundations trends in information retrieval, available at
  3. 3.
    Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 168–177Google Scholar
  4. 4.
    Kim SM, Hovy E (2004) Determining the sentiment of opinions. In: Proceedings of the 20th international conference on computational linguistics. Association for Computational Linguistics, p 1367Google Scholar
  5. 5.
    Wilson T, Hoffmann P, Somasundaran S, Kessler J, Wiebe J, Choi Y … Patwardhan S (2005) OpinionFinder: a system for subjectivity analysis. In:: Proceedings of hlt/emnlp on interactive demonstrations. Association for Computational Linguistics, pp 34–35Google Scholar
  6. 6.
    Bekkerman R, Gavish M (2011) High-precision phrase-based document classification on a modern scale. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 231–239Google Scholar
  7. 7.
    Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision. CS224 N Project Report, Stanford 1(12)Google Scholar
  8. 8.
    Bermingham A, Smeaton AF (2010) Classifying sentiment in microblogs: is brevity an advantage? In: Proceedings of the 19th ACM international conference on Information and knowledge management. ACM, pp 1833–1836Google Scholar
  9. 9.
    Pak A, Paroubek P (2010) Twitter as a corpus for sentiment analysis and opinion mining. In: LREc, vol 10, No. 2010Google Scholar
  10. 10.
    Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R (2011) Sentiment analysis of twitter data. In: Proceedings of the workshop on languages in social media. Association for Computational Linguistics, pp 30–38Google Scholar
  11. 11.
    Barbosa L, Feng J (2010) Robust sentiment detection on twitter from biased and noisy data. In: Proceedings of the 23rd international conference on computational linguistics: posters. Association for Computational Linguistics, pp 36–44Google Scholar
  12. 12.
    Aue A, Gamon M (2005) Customizing sentiment classifiers to new domains: a case study. In: Proceedings of recent advances in natural language processing (RANLP), vol 1, No. 3.1, pp 2–1Google Scholar
  13. 13.
    Zhu X, Ghahramani Z (2002) Learning from labeled and unlabeled data with label propagationGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Monica Malik
    • 1
  • Sameena Naaz
    • 1
    Email author
  • Iffat Rehman Ansari
    • 2
  1. 1.Department of Computer Science and EngineeringSchool of Engineering Sciences and TechnologyNew DelhiIndia
  2. 2.University Women’s Polytechnic, Aligarh Muslim UniversityAligarhIndia

Personalised recommendations