References
- Agarwal, S., Kumar, S. & Goel, U. (2011). Social media and the stock markets: an emerging market perspective, Journal of Business Economics and Management, 22(6), pp. 1614–1632.
https://doi.org/10.3846/jbem.2021.15619 - Aldridge, I. (2017). Real-time risk: What investors should know about fintech, high-frequency trading, and flash crashes. New Jersey: John Wiley & Sons, Inc. ISBN: 978-1-119-31896-5
- Antweiler, W. & Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards, The Journal of Finance, 59, pp. 1259–1294.
https://doi.org/10.1111/j.1540-6261.2004.00662.x - Azar, P. D. & Lo, A. W. (2016). The wisdom of Twitter crowds: Predicting stock market reactions to FOMC meetings via Twitter feeds, The Journal of Portfolio Management, 42(5), pp. 123–134.
https://doi.org/10.3905/jpm.2016.42.5.123 - Barrett, L. F. (2017). The theory of constructed emotion: An active inference account of interoception and categorization. Social Cognitive and Affective Neuroscience, 12(1), 1–23. Oxford University Press.
https://doi.org/10.1093/scan/nsw154 - Bello-Orgaz, G., Jung, J. J. & Camacho, D. (2016). Social big data: recent achievements and new challenges, Information Fusion, 28, pp. 45–59.
https://doi.org/10.1016/j.inffus.2015.08.005 - Bermingham, A. & Smeaton, A. (2010). Classifying sentiment in microblogs: is brevity an advantage?, Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1833–1836. New York, NY: ACM.
- Bollen, J., Mao, H., & Zeng, X. (2011). Twitter Mood Predicts The Stock Market. Journal of Computational Science, 2(1), 1–8.
https://doi.org/10.1016/j.jocs.2010.12.007 - Chen, J., Huang, H., Tian, S. & Qu, Y. (2009). Feature selection for text classification with naïve bayes, Expert Systems with Applications, 36, pp. 5432–5435.
https://doi.org/10.1016/j.eswa.2008.06.054 - Dissanayake, B., Hemachandra, O. & Wijayasiri, A. (2021). A comparison of ARIMAX, VAR, and LSTM on multivariate short-term traffic volume forecasting, Computer Science, Engineering.
- Effrosynidis, D., Symeonidis, S. & Arampatzis, A. (2017). A comparison of pre-processing techniques for Twitter sentiment analysis, 21st International Conference on Theory and Practice of Digital Libraries (TPDL 2017), LNCS 10450, pp. 421–426. Thessaloniki, Greece: Springer.
https://doi.org/10.1007/978-3-319-67008-9_31 - Ekman, P. & Davidson, R. J. (eds.) (2002). Natura emocji. Podstawowe zagadnienia. Gdańsk: Gdańskie Wydawnictwo Psychologiczne.
- Ekman, P. (1972). Universals and cultural differences in facial expressions of emotion, in J. Cole (ed.), Nebraska symposium on motivation, 1971, pp. 207–283. University of Nebraska Press.
https://doi.org/10.1037/0022-3514.53.4.712 - Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work, The Journal of Finance, 25(2), pp. 383–417.
https://doi.org/10.1111/j.1540-6261.1970.tb00518.x - Fischer, T. & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions, European Journal of Operational Research, 270(2), pp. 654–669.
https://doi.org/10.1016/j.ejor.2017.11.054 - Fiszeder, P. (2009). GARCH class models in empirical financial research [eBook]. Torun: Wydawnictwo Naukowe Uniwersytetu Mikołaja Kopernika.
- Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision (CS224N Project Report). Stanford University.
- Ishikawa, H. (2015). Social big data mining. CRC Press.
https://doi.org/10.1201/b18223 - Jena, P. R. & Majhi, R. (2023). Are Twitter sentiments during COVID-19 pandemic a critical determinant to predict stock market movements? A machine learning approach, Scientific African, 19, e01480.
https://doi.org/10.1016/j.sciaf.2022.e01480 - Katsafados, A. G., Nikoloutsopoulos, S. & Leledakis, G. N. (2022). Twitter sentiment and stock market: a COVID-19 analysis, SSRN.
https://doi.org/10.2139/ssrn.3997996 - Keras (n.d.), LSTM layer. Retrieved from
https://keras.io/api/layers/recurrent_layers/lstm/ (28.08.2024). - Krouska, A., Troussas, C. & Virvou, M. (2016). The effect of preprocessing techniques on Twitter sentiment analysis, 7th International Conference on Information, Intelligence, Systems & Applications (IISA), pp. 1–6. IEEE.
https://doi.org/10.1109/IISA.2016.7785373 - Kumari & Mahakud, J. (2015). Does investor sentiment predict the asset volatility? Evidence from emerging stock market India, Journal of Behavioral and Experimental Finance, 8, pp. 1–10.
https://doi.org/10.1016/j.jbef.2015.10.001 - Laney, D. (2001). 3D data management: controlling data volume, velocity, and variety, Meta Group Research Note, 6, pp. 70–73.
- Liu, B. (2012). Sentiment analysis and opinion mining. 1st ed. Springer Cham.
https://doi.org/10.1007/978-3-031-02145-9 - Magliani, F., Fontanini, T., Fornacciari, P., Manicardi, S. & Iotti, E. (2016). A comparison between preprocessing techniques for sentiment analysis in Twitter. Retrieved from ResearchGate:
https://www.researchgate.net/publication/311615347_A_Comparison_between_Preprocessing_Techniques_for_Sentiment_Analysis_in_Twitter (24.08.2024). - Manovich, L. (2011). Trending: the promises and the challenges of big social data. Retrieved from
https://manovich.net/content/old/03-articles/64-article-2011/64-article-2011.pdf (24.08.2024). - Mao, Y., Wei, W., & Liu, B. (2012). Correlating S&P 500 stocks with Twitter data. In Proceedings of the ACM SIGKDD Workshop on Sentiment Analysis in Social Media (pp. 69–72).
https://doi.org/10.1145/2392622.2392634 - Michalak, J. (2021). Opportunities and challenges of big social data analytics based on examples from psychology. In K. S. Soliman (Ed.), Innovation management and information technology impact on global economy in the era of pandemic: Proceedings of the 37th International Business Information Management Association Conference (IBIMA), 30–31 May 2021, Cordoba, Spain (pp. 6374–6381).
- Michalak, J. (2024a). Methodological approaches to sentiment classification and their impact on modeling the relationship between Twitter (X) and the stock market. In A. Dister & D. Longrée (Eds.), JADT 2024: Mots comptés, textes déchiffrés (Vol. 2, pp. 633–642). Presses Universitaires de Louvain.
- Michalak, J. (2024b). Nastroje i emocje użytkowników serwisu Twitter a giełda: modelowanie zależności. Wydawnictwo Naukowe Uniwersytetu Mikołaja Kopernika.
- Michalak, J., & Kruszewski, T. (2021). Pandemia a reakcje inwestorów: analiza big data komunikatorów z Twittera wobec zjawiska niepewności. Toruń: Towarzystwo Naukowe Organizacji i Kierownictwa.
- Nofer, M., & Hinz, O. (2015). Using Twitter to predict the stock market—Where is the mood effect? Business & Information Systems Engineering, 57(4), 229–242.
https://aisel.aisnet.org/bise/vol57/iss4/2 - Nofsinger, J. (2005). Social Mood and Financial Economics. Journal of Behavioral Finance.
- Osińska, M. (2006). Ekonometria finansowa. Warszawa: Polskie Wydawnictwo Ekonomiczne S.A.
- Patil, S., Wangikar, V., & Jayamalini, K. (2017). Tweet data preprocessing and segmentation to NER. W International Conference on Emanations in Modern Technology and Engineering (ICEMTE-2017) (Vol. 5, Issue 3, pp. 172–175). IJRITCC.
- Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
https://jmlr.org/papers/v12/pedregosa11a.html - Rashid, U. & Tanjim, S. (2021). Stock prediction using LSTM and sentiment analysis, IEEE International Conference on Robotics, Automation, and Artificial Intelligence (RAAI), pp. 1–6. IEEE.
- Reinsel, D., Gantz, J., & Rydning, J. (2017). Data Age 2025: The Evolution of Data to Life-Critical. [White paper]. Seagate Technology LLC. Retrieved from
https://www.seagate.com/files/www-content/our-story/trends/files/Seagate-WP-DataAge2025-March-2017.pdf - Sawka, K. (Ed.). (2023). Hands-on machine learning with scikit-learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems (3rd ed.). O'Reilly. ISBN 978-83-832-2423-7.
- Siganos, A., Vagenas-Nanos, E., & Verwijmeren, P. (2014). Facebook's daily sentiment and international stock markets. Journal of Economic Behavior & Organization, 107.
- Sindhu, C., Sasmal, B., Gupta, R., Prathipa, J. (2021). Subjectivity detection for sentiment analysis on Twitter data. In: Hemanth, D., Vadivu, G., Sangeetha, M., Balas, V. (eds) Artificial Intelligence Techniques for Advanced Computing Applications. Lecture Notes in Networks and Systems, vol 130. Singapore: Springer..
https://doi.org/10.1007/978-981-15-5329-5_43 - Thaler, R. H. (2015). Misbehaving: The making of behavioral economics. W Norton & Co.
- Wójcik, A. (2014). Modele wektorowo-autoregresyjne jako odpowiedź na krytykę strukturalnych wielorównaniowych modeli ekonometrycznych. Studia Ekonomiczne, 193, 112–128.
- Yeşiltaş, S., Şen, A., Arslan, B., & Altuğ, S. (2022). A Twitter-Based Economic Policy Uncertainty Index: Expert Opinion and Financial Market Dynamics in an Emerging Market Economy. Frontiers in Physics, 10.
https://doi.org/10.3389/fphy.2022.864207 - Zeitun, R., Ur Rehman, M., Ahmad, N., & Vo, X. V. (2023). The impact of Twitter-based sentiment on US sectoral returns. The North American Journal of Economics and Finance, 64, 101847. ISSN 1062-9408.
https://doi.org/10.1016/j.najef.2022.101847 . - Zobal, V. (2017). Sentiment analysis of social media and its relation to stock market (Bachelor's thesis). Prague: Charles University, Faculty of Social Sciences, Institute of Economic Studies.
