Abstract
In recent years, the public opinion is swayed by online social, media and news platforms, such as Twitter, podcasts, and streaming news broadcasts. The public opinion can alter the outcome of various social-economic events, e.g., the volatility of the stock market. This paper presents an overview of forecasting the volatility of the indices of several companies in the U.S. stock market while considering the sentiment and features extracted from the metadata of a tweet and its author’s social activity and network. The daily changes in the prices of an index in the U.S. stock market were estimated by applying several regression techniques. The results indicate a strong correlation between the approximated closing prices of the stocks in the U.S. stock market, the sentiment along with the features extracted from a tweet, and its author’s activity and network. Finally, the obtained results indicate that the number of attributes did not impact the performance of the applied regression techniques.
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References
Ahuja, R., Rastogi, H., Choudhuri, A., Garg, B.: Stock market forecast using sentiment analysis. In: 2nd IEEE International Conference on Computing for Sustainable Global Development (INDIACom), pp. 1008–1010 (2015)
Ampomah, E.K., Qin, Z., Nyame, G., Botchey, F.E.: Stock market decision support modeling with tree-based adaboost ensemble machine learning models. Informatica (Slovenia) 44(4) (2020)
Baltas, A., Kanavos, A., Tsakalidis, A.K.: An apache spark implementation for sentiment analysis on twitter data. In: 2nd International Workshop on Algorithmic Aspects of Cloud Computing (ALGOCLOUD), vol. 10230, pp. 15–25 (2016)
Barrow, D.K., Crone, S.F.: A comparison of adaboost algorithms for time series forecast combination. Int. J. Forecast. 32(4), 1103–1119 (2016)
Bing, L., Chan, K.C.C., Ou, C.X.: Public sentiment analysis in twitter data for prediction of a company’s stock price movements. In: 11th IEEE International Conference on e-Business Engineering (ICEBE), pp. 232–239 (2014)
Bonta, V., Kumaresh, N., Janardhan, N.: A comprehensive study on lexicon based approaches for sentiment analysis. Asian J. Comput. Sci. Technol. 8(S2), 1–6 (2019)
Chahboun, S., Maaroufi, M.: Performance comparison of support vector regression, random forest and multiple linear regression to forecast the power of photovoltaic panels. In: 9th IEEE International Renewable and Sustainable Energy Conference (IRSEC), pp. 1–4 (2021)
Chicco, D., Warrens, M.J., Jurman, G.: The coefficient of determination r-squared is more informative than smape, mae, mape, mse and rmse in regression analysis evaluation. PeerJ Comput. Sci. 7, e623 (2021)
Das, S., Behera, R.K., Kumar, M., Rath, S.K.: Real-time sentiment analysis of twitter streaming data for stock prediction. Procedia Comput. Sci. 132, 956–964 (2018)
Deveikyte, J., Geman, H., Piccari, C., Provetti, A.: A sentiment analysis approach to the prediction of market volatility. CoRR abs/2012.05906 (2020)
Fumo, N., Biswas, M.A.R.: Regression analysis for prediction of residential energy consumption. Renew. Sustain. Energy Rev. 47, 332–343 (2015)
Guo, X., Li, J.: A novel twitter sentiment analysis model with baseline correlation for financial market prediction with improved efficiency. In: 6th IEEE International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 472–477 (2019)
Gupta, R., Chen, M.: Sentiment analysis for stock price prediction. In: 3rd IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 213–218 (2020)
Hu, J., Gao, P., Yao, Y., Xie, X.: Traffic flow forecasting with particle swarm optimization and support vector regression. In: 17th IEEE International Conference on Intelligent Transportation Systems (ITSC), pp. 2267–2268 (2014)
Jin, F., Wang, W., Chakraborty, P., Self, N., Chen, F., Ramakrishnan, N.: Tracking multiple social media for stock market event prediction. In: 17th Industrial Conference on Advances in Data Mining (ICDM), vol. 10357, pp. 16–30 (2017)
Kanavos, A., Perikos, I., Hatzilygeroudis, I., Tsakalidis, A.K.: Emotional community detection in social networks. Comput. Electr. Eng. 65, 449–460 (2018)
Kanavos, A., Vonitsanos, G., Mohasseb, A., Mylonas, P.: An entropy-based evaluation for sentiment analysis of stock market prices using twitter data. In: 15th IEEE International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), pp. 1–7 (2020)
Khan, W., Ghazanfar, M.A., Azam, M.A., Karami, A., Alyoubi, K.H., Alfakeeh, A.S.: Stock market prediction using machine learning classifiers and social media, news. J. Ambient. Intell. Humaniz. Comput. 13(7), 3433–3456 (2022)
Li, Y., et al.: Random forest regression for online capacity estimation of lithium-ion batteries. Appl. Energy 232, 197–210 (2018)
Lin, K., Lin, Q., Zhou, C., Yao, J.: Time series prediction based on linear regression and SVR. In: 3rd IEEE International Conference on Natural Computation (ICNC), pp. 688–691 (2007)
Liu, Q., Wang, X., Huang, X., Yin, X.: Prediction model of rock mass class using classification and regression tree integrated adaboost algorithm based on tbm driving data. Tunn. Undergr. Space Technol. 106, 103595 (2020)
Maulud, D.H., Abdulazeez, A.M.: A review on linear regression comprehensive in machine learning. J. Appl. Sci. Technol. Trends 1(4), 140–147 (2020)
Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113 (2014)
Mittal, A., Goel, A.: Stock prediction using twitter sentiment analysis. Standford University, CS229 15, 2352 (2012)
Montgomery, D.C., Peck, E.A., Vining, G.G.: Introduction to Linear Regression Analysis. Wiley, Hoboken (2021)
Oliveira, N., Cortez, P., Areal, N.: Some experiments on modeling stock market behavior using investor sentiment analysis and posting volume from twitter. In: 3rd ACM International Conference on Web Intelligence, Mining and Semantics (WIMS), p. 31 (2013)
Rao, T., Srivastava, S.: Analyzing stock market movements using twitter sentiment analysis. In: International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2012)
Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., Chica-Rivas, M.: Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geol. Rev. 71, 804–818 (2015)
Sahayak, V., Shete, V., Pathan, A.: Sentiment analysis on twitter data. Int. J. Innovative Res. Adv. Eng. (IJIRAE) 2(1), 178–183 (2015)
Sharma, V., Khemnar, R., Kumari, R., Mohan, B.R.: Time series with sentiment analysis for stock price prediction. In: 2nd IEEE International Conference on Intelligent Communication and Computational Techniques (ICCT), pp. 178–181 (2019)
Souza, T.T.P., Kolchyna, O., Treleaven, P.C., Aste, T.: Twitter sentiment analysis applied to finance: a case study in the retail industry. CoRR abs/1507.00784 (2015)
Xu, M., Watanachaturaporn, P., Varshney, P.K., Arora, M.K.: Decision tree regression for soft classification of remote sensing data. Remote Sens. Environ. 97(3), 322–336 (2005)
Yao, J.: Automated sentiment analysis of text data with nltk. J. Phys. Conf. Ser. 1187, 052020 (2019)
Acknowledgement
This research was co-financed by the European Union and Greek national funds through the “Competitiveness, Entrepreneurship and Innovation” Operational Programme 2014–2020, under the Call “Support for regional excellence”; project title: “Intelligent Research Infrastructure for Shipping, Transport and Supply Chain - ENIRISST+”; MIS code: 5047041.
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Saravanos, C., Kanavos, A. (2023). Forecasting Stock Market Alternations Using Social Media Sentiment Analysis and Regression Techniques. In: Maglogiannis, I., Iliadis, L., Papaleonidas, A., Chochliouros, I. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 677. Springer, Cham. https://doi.org/10.1007/978-3-031-34171-7_27
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