The Impact of Social Networks on the Stock Market Using Sentiment Analysis and Machine Learning: Application to the Turkish Stock Market

Year 2025, Volume: 9 Issue: 1, 253 - 274, 30.06.2025

Mustafa Kemal Mayuk , Farid Huseynov

https://doi.org/10.26650/acin.1616088

Abstract

The proliferation of portable devices and social media has transformed opinion sharing, impacting individual behavior, particularly in financial markets. This research explores how online sentiments influence investors’ decisionmaking, highlighting the complexities of sentiment measurement in behavioral finance. AIdriven techniques have been developed to quantify opinions from social media data, focusing on Twitter (rebranded as X). The transformer architecture, which is a cutting edge deep learning method widely used in generative AI models, is employed for sentiment analysis. The relationship between digitized sentiment scores and share prices within Türkiye’s Borsa İstanbul (BIST 30) index was analyzed using machine learning techniques. Social media activity, as indicated by tweet volume, was investigated in relation to stock prices. The dataset comprises nearly 1.9 million tweets related to BIST 30 stocks, collected from early 2021 to late 2022. Independent variables include tweet volume, sentiment (positive negative), and tweet timing, whereas dependent variables comprise stock prices and index closures. The findings reveal that tweet volume effectively predicts stock prices. Positive sentiment demonstrates stronger predictive power for individual stocks, whereas overall tweet sentiment does not significantly affect index wide prices. Conversely, tweet timing is ineffective for price prediction. This research exemplifies the growing application of AI and machine learning in the social sciences by quantifying human opinions. The proposed model offers both theoretical and practical contributions, serving as a model for future research while delivering new insights and recommendations. The insights gained underscore the potential to harness information systems to advance financial literacy, stimulate economic growth, and empower informed decisionmaking across diverse global contexts.

Keywords

Artificial Intelligence , Machine Learning , Sentiment Analysis , Stock Market Forecasting , Social Networks

References

• Ahn, H., & Kim K. J. (2009). Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach. Applied Soft Computing, 9(2), 599–607. https://doi.org/10.1016/j.asoc.2008.08.002. google scholar
• Almansour, B. Y., Elkrghli, S., & Almansour, A. Y. (2023). Behavioral finance factors and investment decisions: A mediating role of risk perception. Cogent Economics & Finance, 11(2). https://doi.org/10.1080/23322039.2023.2239032. google scholar
• Altınbaş, H. Y. (2022). COVID-19’s Impact on Global Stock Market Movements and an Examination on Turkish Market. Yıldız Social Science Review, 8(1). https://doi.org/10.51803/yssr.1146870. google scholar
• Bahoo, S., Cucculelli, M., Goga, X., & Mondolo, J. (2024). Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis. SN Bus Econ, 4(23). https://doi.org/10.1007/s43546-023-00618-x. google scholar
• Bentes, S. R., & Navas, R. (2013). The Fundamental Analysis: An Overview. International Journal of Latest Trends in Finance and Economic Sciences, 3(1), 389-393. google scholar
• Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(2011), 1-8. https://doi. org/10.1016/j.jocs.2010.12.007. google scholar
• Cao, L. (2022). AI in Finance: Challenges, Techniques, and Opportunities. ACM Computing Surveys (CSUR), 55(3), 1-38. https://doi.org/10. 48550/arXiv.2107.09051. google scholar
• Dunis, C. L., Middleton, P. W., Theofilatos, K., & Karathanasopoulos, A. (2016). Artificial Intelligence in Financial Markets: Cutting Edge Applications for Risk Management, Portfolio Optimization and Economics. Palgrave Macmillan google scholar
• Effrosynidis D., Symeonidis S., & Arampatzis A., (2017 September). A Comparison of Preprocessing google scholar
• Techniques for Twitter Sentiment Analysis. 21st International Conference on Theory and Practice of Digital Libraries, Thessaloniki, Greece, 2019. google scholar
• Guo, K., & Xie, H. (2024). Deep learning in finance assessing Twitter sentiment impact and prediction on stocks. PeerJ Computer Science, 10(2018). https://doi.org/10.7717/peerj-cs.2018. google scholar
• Hayran, A., & Sert, M. (2017 May). Sentiment Analysis on Microblog Data based on Word Embedding and Fusion Techniques. IEEE 25th Signal Processing and Communications Applications Conference (SIU 2017), Antalya, Türkiye. google scholar
• Hugging Face. (2023). BERT Base Turkish Sentiment. Retrieved from https://huggingface.co/savasy/bert-base-turkish-sentiment-cased. Accessed on March 10, 2023. google scholar
• Investing. (2024). BIST 30 Index. Retrieved from https://tr.investing.com/indices/ise-30. Access Date: April 30, 2024. google scholar
• Is Yatirim. (2024). Historical prices. Retrieved from https://www.isyatirim.com.tr/tr-tr/analiz/hisse/Sayfalar/Tarihsel-Fiyat-Bilgileri.aspx. Access Date: April 30, 2024. google scholar
• Kearney, C., & Liu, S. (2014). Textual sentiment in finance: A survey of methods and models. International Review of Financial Analysis, 33(2014), 171-185. https://doi.org/10.1016/j.irfa.2014.02.006. google scholar
• Kına, E., & Biçek, E. (2024). Machine Learning Approach for Emotion Identification and Classification in Bitcoin Sentiment Analysis. Yuzuncu Yil University Journal of the Institute of Natural & Applied Sciences, 29(3), 913-926. https://doi.org/10.53433/yyufbed. 1532649 google scholar
• Kumar, A. (2009). Hard-To-Value Stocks, Behavioral Biases, and Informed Trading. Journal of Financial and Quantitative Analysis, 44, 1375-1401. https://dx.doi.org/10.2139/ssrn.903820. google scholar
• Lee, C., Gao, Z., & Tsai, C. (2020 September). BERT-Based Stock Market Sentiment Analysis. 2020 IEEE International Conference on Consumer Electronics, Taoyuan, Taiwan. google scholar
• Li, X., Xie, H., Chen, L., Wanga, J., & Deng, X. (2014). News impact on stock price return via sentiment analysis. Knowledge-Based Systems, 69(2014), 14-23. https://doi.org/10.1016/j.knosys.2014.04.022. google scholar
• Liu, Z., Yang, L., & Takada, T. (2023 July). Predicting Stock Prices Using Tweet Frequency and AI: Leveraging Social Media Insights to Forecast Tomorrow's Market Trends. 12th International Conference on Digital Image Processing and Vision, London, United Kingdom. google scholar
• Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan and Claypool Publishers. google scholar
• Motiwalla, L., & Wahab, M. (2000). Predictable variation and profitable trading of US equities: A trading simulation using neural networks. Computers & Operations Research, 27, 1111–1129. https://doi.org/10.1016/S0305-0548(99)00148-3. google scholar
• Namin, S. S., & Namin, A. S. (2018). Forecasting Economic And Financial Time Series: ARIMA vs. LSTM. Lubbock, TX, USA: Texas Tech University; 2008. doi:https://arxiv.org/ftp/arxiv/papers/1803/1803.06386.pdf google scholar
• Naseer, M., & Tariq, Y. B. (2015). The Efficient Market Hypothesis: A Critical Review of the Literature. The IUP Journal of Financial Risk Management, 12(4), 48-63. google scholar
• Ozili, P. K. (2023). The acceptable R-square in empirical modelling for social science research. Munich Personal RePEc Archive. https:// mpra.ub.uni-muenchen.de/id/eprint/116496 google scholar
• Pattanayak, A. M., Swetapadma, A., & Sahoo, B. (2024). Exploring Different Dynamics of Recurrent Neural Network Methods for Stock Market Prediction - A Comparative Study. Applied Artificial Intelligence, 38(1), https://doi.org/10.1080/08839514.2024.2371706. google scholar
• Rao, T., & Srivastava, S. (2012 August). Analyzing Stock Market Movements Using Twitter Sentiment Analysis. International Conference on Advances in Social Networks Analysis and Mining, Istanbul, Türkiye. google scholar
• Ruan, Q., Wang, Z., Zhou, Y., & Lv, D. (2020). A new investor sentiment indicator (ISI) based on artificial intelligence: A powerful return predictor in China. Economic Modelling, 88(2020), 47-58. https://doi.org/10.1016/j.econmod.2019.09.009. google scholar
• Schumaker R.P., Zhang Y., & Huang C.H. (2012). Evaluating sentiment in financial news articles. Decision Support Systems, 53(3), 458-464. https://doi.org/10.1016/j.dss.2012.03.001. google scholar
• Shah, D., Isah, H., & Zulkernine, F. (2019). Stock Market Analysis: A Review and Taxonomy of Prediction Techniques. International Journal of Financial Studie, 7(2), 26–33. https://doi.org/10.3390/ijfs7020026. google scholar
• Shubham, A., Kumar, N., Rathee, G., Kerrache, C., Calafate, C., & Bilal, M. (2024). Improving stock market prediction accuracy using sentiment and technical analysis. Electronic Commerce Research, 1-24. https://doi.org/10.1007/s10660-024-09874-x. google scholar
• Sohangir, S., Wang, D., Pomeranets, A., & Khoshgoftaar, T. M. (2018). Big Data: Deep learning models for financial sentiment analysis. Journal of Big Data, 5(1), 1-25. https://doi.org/10.1186/s40537-017-0111-6. google scholar
• Souza, T. T. P., Kolchyna, O., Treleaven, P.C., & Aste, T. (2015). Twitter Sentiment Analysis Applied to Finance: A Case Study in the Retail Industry. ArXiv:1507.00784. google scholar
• Thawornwong, S., & Enke, D. (2004). The adaptive selection of financial and economic variables for use with artificial neural networks. Neurocomputing, 56, 205–232. https://doi.org/10.1016/j.neucom.2003.05.001. google scholar
• Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017 December). Attention Is All You Need. 31st International Conference on Neural Information Processing Systems, Long Beach, California, USA, 2019. google scholar
• White, H. (1988 July). Economic prediction using neural networks: The case of IBM daily stock returns. Second IEEE annual conference on neural networks, San Diego, CA, USA. google scholar
• Yıldırım, S. (2024). Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks (ArXiv:2401.17396). google scholar
• Yu, Y., Duan, W., & Cao, Q. (2012). The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decision Support Systems, 55(4), 919-926. https://doi.org/10.1016/j.dss.2012.12.028. google scholar
• Zhang, X., Fuehres, H., & Gloor P. A. (2011). Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear”. Procedia - Social and Behavioral Sciences, 26(2011), 55-62. https://doi.org/10.1016/j.sbspro.2011.10.562. google scholar