Twitter Sentimental Analysis on Fan Engagement

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 645)

Abstract

Sentimental analysis involves determination of opinions, feelings, and subjectivity of text. Twitter is a social networking service where millions of people share their thoughts. Twitter sentimental analysis on fan engagement focuses on how fans in different sports industry actively engage on social media. Fans are identified based on their opinions and emotions expressed in the tweet. In this paper, we provide a comparative analysis of machine learning algorithms such as multinomialNB, support vector machine, linearSVM, and decision trees. The traditional approach involves manually assigning labels to training data which is time-consuming. To overcome this problem, we use TextBlob which computes the sentiment polarity based on POS tagging and assigns sentiment score to every tweet in range of −1 to +1. We compute the subjectivity of text which is in the range of 1–0. We also identify the sarcastic tweets and assign correct class labels based on the weightage we provide for every word. Our experimental result shows how accuracy gets increased with the use of advanced machine learning algorithms.

Keywords

Twitter Sentimental analysis Machine learning Analytics Naïve bayes MultinomialNB Support vector machine LinearSVM Decision trees Polarity analysis MongoDB Python 

References

  1. 1.
    Pang, P., Lee, L., Vaithyanathan, S.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts (2004)Google Scholar
  2. 2.
    Loper, E., Bird, S.: NLTK libraryGoogle Scholar
  3. 3.
    Wang, H., Can, D., Bar, F., Narayana, S.: Sentimental analysis of twitter data using NLTKGoogle Scholar
  4. 4.
    Almatrafi, O., Parack, S., Chavan, B.: Combining rule based classifiersGoogle Scholar
  5. 5.
    Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent Twitter Sentiment ClassificationGoogle Scholar
  6. 6.
    Tan, C., Lee, L., Tang, J., Jiang, L., Zhou, M., Li, P.: Extraction and mining of academic social networksGoogle Scholar
  7. 7.
    Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of LREC (2010)Google Scholar
  8. 8.
    Minging, H., Bing, L.: Mining and summarizing customer reviews. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’04) (2004)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.
    Go, A., Bhayani R., Huang L.: Twitter sentiment classification using distant supervision. Technical Paper, Stanford University (2009)Google Scholar
  11. 11.
    Bifet, A., Frank, E.: Sentiment knowledge discovery in twitter streaming data. In: Proceedings of the 13th International Conference on Discovery Science, pp. 1–15. Springer, Berlin, Germany (2010)Google Scholar
  12. 12.
    Neethu, M.S., Rajashree, R.: Sentiment analysis in twitter using machine learning techniques. In: 4th ICCCNT. Tiruchengode, India. IEEE—31661 (2013)Google Scholar
  13. 13.
    Peddinti, V.M.K., Chintalapoodi, P.: Domain adaptation in sentiment analysis of twitter. In: Analyzing Microtext Workshop, AAAI (2011)Google Scholar
  14. 14.
    Dumais, S. et al.: Inductive learning algorithms and representations for text categorization. In: Proceedings of the Seventh International Conference on Information and Knowledge Management. ACM (1998)Google Scholar
  15. 15.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Technical Report, Stanford (2009)Google Scholar
  16. 16.
    Wilson, T., Wiebe, J., Hoffman, P.: Recognizing contextual polarity in phrase level sentiment analysis. ACL (2005)Google Scholar
  17. 17.
    Manning, C.D., Schutze, H.: Foundations of statistical natural language processing. MIT Press (1999)Google Scholar
  18. 18.
    Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the Conference on Web Search and Web Data Mining (WSDM) (2008)Google Scholar
  19. 19.
    Popescu, A.-M., Etzioni, O.: Extracting Product Features and Opinions from Reviews. EMNLP-05 (2005)Google Scholar
  20. 20.
    Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of International Conference on Machine Learning (ICML’01) (2001)Google Scholar
  21. 21.
    Lund, K., Burgess, C.: Producing high-dimensional semantic spaces from lexical co-occurrence. Behav. Res. Methods 28, 203–238 (1996)CrossRefGoogle Scholar
  22. 22.
    Zhou, L., Li, B., Gao, W., Wei, Z., Wong, K.: Unsupervised discovery of discourse relations for eliminating intra-sentence polarity ambiguities. In: Presented at the 2001 Conference on EmpiricalGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Information Technology (Big Data Analytics)SRM UniversityChennaiIndia
  2. 2.Department of Information TechnologySRM UniversityChennaiIndia

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