Tweet Analyzer: Identifying Interesting Tweets Based on the Polarity of Tweets

  • M. Arun Manicka RajaEmail author
  • S. Swamynathan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 410)


Sentiment analysis is the process of finding the opinions present in the textual content. This paper proposes a tweet analyzer to perform sentiment analysis on twitter data. The work mainly involves the sentiment analysis process using various trained machine learning classifiers applied on large collection of tweets. The classifiers have been trained using maximum number of polarity oriented words for effectively classifying the tweets. The trained classifiers at sentence level outperformed the keyword based classification method. The classified tweets are further analyzed for identifying top N tweets. The experimental results show that the sentiment analyzer system predicted polarities of tweet and effectively identified top N tweets.


Tweets Sentiment analysis Opinion polarity Classification Top N tweets 


  1. 1.
    Kim, Y., Shim. K.: TWITOBI a recommendation system for twitter using probabilistic modeling. In: International Conference on Data Mining, pp. 340–349. IEEE (2011)Google Scholar
  2. 2.
    Neethu, M.S., Rajasree, R.: Sentiment analysis in twitter using machine learning techniques, computing. In: International Conference on Communications and Networking Technologies, pp. 1–5. IEEE (2013)Google Scholar
  3. 3.
    Rill, V., Reinel, D., Scheidt, J., Zicari, R. V.: PoliTwi early detection of emerging political topics on twitter and the impact on concept-level sentiment analysis. J. Knowl. Based Syst. 69, 24–33 (2014). (ScienceDirect)Google Scholar
  4. 4.
    Cagliero, L., Fiori, A.: TweCoM topic and context mining from twitter. In: The Influence of Technology on Social Network Analysis and Mining, vol. 6, pp. 75–100. Springer (2013)Google Scholar
  5. 5.
    Liu, S., Cheng, X., Li, F., Li, F.: TASC: topic-adaptive sentiment classification on dynamic tweets. Trans. Knowl. Data Eng. 27(6), 1696–1709 (2015). (IEEE)Google Scholar
  6. 6.
    Gautam, G., Yadav, D.: Sentiment analysis of twitter data using machine learning approaches and semantic analysis. In: International Conference Contemporary Computing, pp. 437–442. IEEE (2014)Google Scholar
  7. 7.
    Pennacchiotti, M., Popescu, A.: A machine learning approach to twitter user classification. In: Proceedings of the International Conference on Weblogs and Social Media, pp. 281–288. (2011)Google Scholar
  8. 8.
    Arun Manicka Raja, M., Winster, S.G., Swamynathan, S.: Review analyzer analyzing consumer product reviews from review collection. In: International Conference on Recent Advances in Computing and Software Systems, pp. 287–292. IEEE (2012)Google Scholar
  9. 9.
    Lin, J., Kolcz, A.: Large-scale machine learning at twitter. In: International Conference on Management of Data. Proceedings of the ACM SIGMOD, pp.793–804. USAGoogle Scholar
  10. 10.
    Yang, M.-C., Rim, H.-C.: Identifying interesting twitter contents using topical analysis. J. Expert Syst. Appl. 41(9), 4330–4336 (2014). ScienceDirectCrossRefGoogle Scholar
  11. 11.
    Aiello, L.M., Petkos, G., Martin, C., Corney, D., Papadopoulos, S., Skraba, R., Goker, A., Kompatsiaris, I., Jaimes, A.: Sensing trending topics in twitter. Trans. Multimedia. 15, 61268–61282 (2013)Google Scholar
  12. 12.
    Kim, Y., Shim, Kyuseok: TWILITE: a recommendation system for Twitter using a probabilistic model based on latent Dirichlet allocation. Journal of Information Systems ACM 42, 59–77 (2014)CrossRefGoogle Scholar
  13. 13.

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Information Science and TechnologyCollege of Engineering Guindy, Anna UniversityChennaiIndia

Personalised recommendations