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Analysis and Prediction of Stock Market Using Twitter Sentiment and DNN

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Intelligent Computing, Information and Control Systems (ICICCS 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1039))

Abstract

The analysis of market is one of the important tasks for data analysts. The stock market is very volatile and many models are developed to predict the market by training the model on historical data. But sentimental analysis approach to predict the market is not common. Here we are using REST APIs to capture the twitter data and sentiment analysis using Google’s Natural Language Cloud API along with machine learning techniques like linear regression, Random forest and DNN are used to predict the bear and bullish curves of the stock data. It was found that positive response in the tweets is observed to result in a rise in market value and negative tweets results in fall of the market value. Neural-networks with Feed-forward technique are found to predict prices of stocks with less error in comparison with other used prediction techniques.

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References

  1. Asur, S., Huberman, B.A.: Predicting the Future with social media. In: Proceedings of the ACM International Conference on Web Intelligence, pp. 492–499 (2010)

    Google Scholar 

  2. Aramaki, E., Maskawa, S., Morita, M.: Twitter catches the flu: detecting influenza epidemics using Twitter. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (2011)

    Google Scholar 

  3. Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)

    Article  Google Scholar 

  4. Qian, B., Rasheed, K.: Stock market prediction with multiple classifiers. Appl. Intell. 26(1), 25–33 (2007). https://doi.org/10.1007/s10489-006-0001-7

    Article  Google Scholar 

  5. Fama, E.F.: The behavior of stock-market prices. J. Bus. 38(1), 34105 (1965). https://doi.org/10.2307/2350752

    Article  Google Scholar 

  6. El Alaoui, I., Gahi, Y., Messoussi, R., et al.: J. Big Data 5, 12 (2018). https://doi.org/10.1186/s40537-018-0120-0

    Article  Google Scholar 

  7. Mizumoto, K., Yanagimoto, H., Yoshioka, M.: Sentiment analysis of stock market news with semi-supervised learning. In: 2012 IEEE/ACIS 11th International Conference on Computer and Information Science, Shanghai, pp. 325–328 (2012). https://doi.org/10.1109/icis.2012.97

  8. Jansen, B., Zhang, M., Sobel, K., Chowdury, A.: Twitter power: tweets as electronic word of mouth. J. Am. Soc. Inform. Sci. Technol. 60, 2169–2188 (2009)

    Article  Google Scholar 

  9. Maini, S.S., Govinda, K.: Stock market prediction using data mining techniques. In: 2017 International Conference on Intelligent Sustainable Systems (ICISS), Palladam, pp. 654–661 (2017). https://doi.org/10.1109/iss1.2017.8389253

  10. Pagolu, V.S., Reddy, K.N., Panda, G., Majhi, B.: Sentiment analysis of Twitter data for predicting stock market movements. In: 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), Paralakhemundi, pp. 1345–1350 (2016). https://doi.org/10.1109/scopes.2016.7955659

  11. Tiwari, S., Bharadwaj, A., Gupta, S.: Stock price prediction using data analytics. In: 2017 International Conference on Advances in Computing, Communication and Control (ICAC3), Mumbai, pp. 1–5 (2017). https://doi.org/10.1109/icac3.2017.8318783

  12. El Alaoui, I., Gahi, Y., Messoussi, R., Chaabi, Y., Todoskoff, A., Kobi, A.: A novel adaptable approach for sentiment analysis on big social data. J. Big Data 5 (2018). https://doi.org/10.1186/s40537-018-0120-0

  13. Home page of Yahoo Finance. https://in.finance.yahoo.com/. Accessed 30 Mar 2019

  14. Home page of Twitter. https://developer.twitter.com/. Accessed 30 Mar 2019

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Author information

Authors and Affiliations

  1. Vellore Institute of Technology, Vellore, India

    T. P. Sahana & J. Anuradha

Authors
  1. T. P. Sahana
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  2. J. Anuradha
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Corresponding author

Correspondence to T. P. Sahana .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Vaigai College of Engineering, Madurai, Tamil Nadu, India

    A. Pasumpon Pandian

  2. Electrical and Computer Engineering, University of Applied Sciences, Egaleo, Attiki, Greece

    Klimis Ntalianis

  3. Department of Business Administration, The Gerald Schwartz School of Business, Antigonish, NS, Canada

    Ram Palanisamy

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Sahana, T.P., Anuradha, J. (2020). Analysis and Prediction of Stock Market Using Twitter Sentiment and DNN. In: Pandian, A., Ntalianis, K., Palanisamy, R. (eds) Intelligent Computing, Information and Control Systems. ICICCS 2019. Advances in Intelligent Systems and Computing, vol 1039. Springer, Cham. https://doi.org/10.1007/978-3-030-30465-2_5

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