Measuring the Influence of Moods on Stock Market Using Twitter Analysis

  • Sanjeev K. CowlessurEmail author
  • B. Annappa
  • B. Kavya Sree
  • Shivani Gupta
  • Chandana Velaga
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 863)


It is a well-known fact that sentiments play a vital role and is an incredibly influential tool in several aspects of human life. Sentiments also drive proactive business solutions. Studies have shown that the more appropriate data is gathered and analyzed at the right time, the higher the success of sentiment analysis. This paper analyses the correlation between the public mood and the variation in stock prices towards companies in different domains. For each tweet, scores are assigned to eight predefined moods namely “Joy”, “Sadness”, “Fear”, “Anger”, “Trust”, “Disgust”, “Surprise” and “Anticipation”. A regression model is applied to the mood scores and the stock prices dataset to obtain the R-squared score, which is a metric used to evaluate the model. The paper aims to find the moods that best reflect the stock values of the respective companies. From the results, it is observed that there is a definite correlation between public mood and stock market.


Mood Lexicon Regression Twitter analysis Stock market Sentiment analysis 


  1. 1.
    Siew, H.L., Nordin, M.J.: Regression techniques for the prediction of stock price trend. In: International Conference on Statistics in Science, Business and Engineering, Langkawi, pp. 1–5 (2012)Google Scholar
  2. 2.
    Mostafa, M.M.: More than words: social networks text mining for consumer brand sentiments. Expert Syst. Appl. 40(10), 4241–4251 (2013)CrossRefGoogle Scholar
  3. 3.
    Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2, 1–8 (2011)CrossRefGoogle Scholar
  4. 4.
    Gilbert, E., Karahaalios, K.: Widespread worry and the stock market. In: Proceedings of the Fourth International Conference on Weblogs and Social Media, pp. 58–65 (2010)Google Scholar
  5. 5.
    Granger, C.W.J.: Some recent development in a concept of causality. J. Econometrics 39(12), 199–211 (1988)MathSciNetCrossRefGoogle Scholar
  6. 6.
    De Choudhury, M., Sundaram, H., John, A., Seligmann, D.D.: Can blog communication dynamics be correlated with stock market activity? In: Proceedings of the Nineteenth ACM Conference on Hypertext and hypermedia, pp. 55–60 (2008)Google Scholar
  7. 7.
    Zhang, X., Fuehres, H., Gloor, P.A.: Predicting stock market indicators through twitter “I Hope It Is Not as Bad as I Fear”. In: Proceedings of Collaborative Innovations Networks Conference, pp. 1–8 (2010)Google Scholar
  8. 8.
    Gloor, P., Krauss, J., Nann, S., Fischbach, K., Schoder, D.: Web science 2.0: identifying trends through semantic social network analysis. In: IEEE Conference on Social Computing, Vancouver (2009)Google Scholar
  9. 9.
    Gloor, P.A.: Forecasting social movements by web buzz analysis. In: Proceedings of the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research, ACM, New York, pp. 32–32 (2012)Google Scholar
  10. 10.
    Mohammad, S.M., Turney, P.D.: Emotions evoked by common words and phrases: using mechanical turk to create an emotion lexicon. In: Proceedings of the Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 26–34. ACL (2010)Google Scholar
  11. 11.
    Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Pennebaker, J.W., Booth, R.J., Francis, M.E.: LIWC2007: Linguistic Inquiry and Word Count. Austin, Texas (2007)Google Scholar
  13. 13.
    Hogenboom, A., Bal, D., Frasincar, F., Bal, M., de Jong, F.M.G., Kaymak, U.: Exploiting emoticons in sentiment analysis. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing, pp. 703–710 (2013)Google Scholar
  14. 14.
    Hasan, M., Rundensteiner, E., Agu, E.: EMOTEX: detecting emotions in twitter messages. In: Proceedings of the 6th ASE International Conference on Social Computing, Academy of Science and Engineering (2014)Google Scholar
  15. 15.
    Kralj, N.P., Smailovi, J., Sluban, B., Mozeti, I.: Sentiment of emojis. PLoS One 10(12), 1–22 (2015)Google Scholar
  16. 16.
    Gupta, N., Abhinav, K.R., Annappa, B.: Fuzzy sentiment analysis on microblogs for movie revenue prediction. In: International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications, The Oxford College of Engineering, Bangalore, IEEEXplore, pp. 1–4. (2013)
  17. 17.
    Nidhi, R.H., Annappa, B.: Twitter-user recommender system using tweets: a content-based approach. In: International Conference on Computational Intelligence in Data Science(ICCIDS), Chennai, pp. 1–6. (2017)

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Sanjeev K. Cowlessur
    • 1
    Email author
  • B. Annappa
    • 2
  • B. Kavya Sree
    • 2
  • Shivani Gupta
    • 2
  • Chandana Velaga
    • 2
  1. 1.Department of Software EngineeringUniversité des MascareignesBeau Plan, PamplemoussesMauritius
  2. 2.Department of Computer Science and EngineeringNational Institute of Technology KarnatakaSurathkalIndia

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