REFLECTİON OF SOCİAL MEDİA SENTİMENTS IN FİNANCİAL MARKETS: SENTİMENT ANALYSİS OF SPORTS STOCKS WİTH TELEGRAM DATA
SOSYAL MEDYA DUYGULARININ FİNANSAL PİYASALARA YANSIMASI: TELEGRAM VERİLERİYLE SPOR HİSSELERİNDE DUYGU ANALİZİ
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https://doi.org/10.5281/zenodo.17930152Keywords:
Sentiment Analysis, Social Media, Behavioral Finance, Text MiningAbstract
This study examines sentiment data obtained from messages shared in Telegram investor groups to explore the potential impact of social media sentiment on the price movements of sports stocks. A total of 33,281 messages related to Beşiktaş, Fenerbahçe, Galatasaray, and Trabzonspor stocks were analyzed. Using the Orange Text Mining infrastructure, the messages were categorized into three sentiment categories, positive, negative, and neutral. The classification results indicate a significant predominance of neutral content in the dataset (approximately 62%), while expressions of positive and negative sentiment were limited. Daily logarithmic returns for the selected stocks were then calculated and matched with sentiment scores based on date to assess potential relationships. Regression analyses revealed that social media based sentiment scores did not have a significant or consistent impact on the daily returns of sports stocks. The findings suggest that the low diversity of sentiment on short, context limited, and conversation oriented platforms like Telegram limits both classification models and financial relationship analyses. The study provides an original contribution to the field of social media sentiment analysis for sports stocks in Turkey and highlights the need for more comprehensive datasets in the future.
References
Akba, F., Uçan, A., Sezer, E. A., & Sever, H. (2014). Assessment of feature selection metrics for sentiment analyses: Turkish movie reviews. European Conference on Data Mining, 180-184.
Akdoğan, Y. E., & Anbar, A. (2024). More than just sentiment: Using social, cognitive, and behavioral information of social media to predict stock markets with artificial ıntelligence and big data. Borsa Istanbul Review, 24, 61-82. https://doi.org/10.1016/j.bir.2023.11.001.
Appel O., Chiclana F., Carter J., & Fujita H. (2016). A hybrid approach to sentiment analysis. 2016 IEEE Congress on Evolutionary Computation (CEC), IEEE, 4950-7.
Cam, H., Cam, A. V., Demirel, U., &Ahmed, S. (2024). Sentiment analysis of financial twitter posts on twitter with the machine learning classifiers. Heliyon, 10(1).
Campbell, J. Y., Lo, A. W., MacKinlay, A. C., & Whitelaw, R. F. (1998). The econometrics of financial markets. Macroeconomic Dynamics, 2(4), 559-562.
Catal, C., & Nangir, M. (2017). A sentiment classification model based on multiple classifiers. Applied Soft Computing, 50, 135-141.
Çoban, Ö., Özyer, B., & Özyer, G. T. (2015). Sentiment analysis for Turkish twitter feeds. In Signal Processing and Communications Applications Conference (pp. 2388-2391). IEEE.
Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129-152. https://doi.org/10.1257/jep.21.2.129.
Bernile, G., & Lyandres, E. (2011). Understanding ınvestor sentiment: The case of soccer. Financial Management, 40(2), 357-380. https://doi.org/10.1111/j.1755-053X.2011.01141.x.
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.
Demirhan, D. (2013). Stock market reaction to national sporting success: Case of Borsa Istanbul. Pamukkale Journal of Sport Sciences, 4(3), 107-121.
Edmans, A., García, D., & Norli, Ø. (2007). Sports sentiment and stock returns. Journal of Finance, 62(4), 1967-1998. https://doi.org/10.1111/j.1540-6261.2007.01262.x.
Esen, E., Özdemir, A., & Temizel, F. (2020). Borsa İstanbul 100 endeksinde yer alan şirketlerin sosyal medya kullanımı ve finansal performans ilişkisi. Uluslararası Yönetim İktisat ve İşletme Dergisi, 16(1), 150-159. https://doi.org/10.17130/ijmeb.2020.16.1.596.
Feldman, R. (2013).Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89.
Field, A. (2024). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
Go, A., Huang, L., & Bhayani, R. (2009). Twitter sentiment analysis. Entropy, 17, 252.
Hamraoui, I., & Boubaker, A. (2022). Impact of Twitter Sentiment on Stock Price Returns. Social Network Analysis and Mining, 12(1), 28. https://doi.org/10.1007/s13278-022-00856-7.
He, H., & Garcia, E. A. (2009). Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263-1284.
Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression. John Wiley and Sons.
Hsieh, Y. Y., Vergne, J. P., Anderson, P., Lakhani, K., & Reitzig, M. (2018). Bitcoin and the rise of decentralized autonomous organizations. Journal of Organization Design, 7(1), 1-16. https://doi.org/10.1186/s41469-018-0039-1.
Inuduka, T., Yokose, A., & Managi, S. (2024). Noise trader impact: bitcoin market evidence from Telegram and X. social network analysis and mining. Advance Online Publication. https://doi.org/10.1007/s13278-024-01245-5.
Kaya, M. (2013). Sentiment analysis of Turkish political columns with transfer learning [Master’s thesis]. Middle East Technical University.
Liu, B. (2022). Sentiment analysis and opinion mining. Springer Nature.
Manning, C. D. (2008). Introduction to information retrieval. Syngress Publishing.
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A Survey. Ain Shams engineering journal, 5(4), 1093-1113.
Naeem, M. A., Mbarki, I., Suleman, M. T., Vo, X. V., & Shahzad, S. J. H. (2021). Does Twitter happiness sentiment predict cryptocurrency?. International Review of Finance, 21(4), 1529-1538.
Najafi, A. (2024). Developing Turkish language models on social media [Unpublished master’s thesis]. Sabancı University.
Nassirtoussi, A. K., Aghabozorgi, S., Wah, T. Y., & Ngo, D. C. L. (2014). Text mining for market prediction: A systematic review. Expert Systems with Applications, 41(16), 7653-7670.
Pak, A., & Paroubek, P. (2010, May). Twitter as a corpus for sentiment analysis and opinion mining. In LREc, 10(2010), 1320-1326.
Palomino, F., Renneboog, L., & Zhang, C. (2009). Information salience, investor sentiment, and stock returns: The case of British soccer betting. Journal of Corporate finance, 15(3), 368-387.
Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1), 1-135.
Ranco, G., Aleksovski, D., Caldarelli, G., Grčar, M., & Mozetič, I. (2015). The effects of twitter sentiment on stock price returns. PLOS ONE, 10(9), e0138441. https://doi.org/10.1371/journal.pone.0138441.
Sevinç, D., & Coşkun, M. (2025). The ımpact of social media and ınternet forums posts on the stock market dynamics: A sentiment analysis using a lexical approach in Borsa İstanbul. Applied Economics. Advance online publication.
Smuts, N. (2019). What Drives Cryptocurrency Prices? An Investigation of Google Trends and Telegram Sentiment. ACM SIGMETRICS Performance Evaluation Review, 46(3), 131-134. https://doi.org/10.1145/3308897.3308955.
Sprenger, T. O., Tumasjan, A., Sandner, P. G., ve Welpe, I. M. (2014). Tweets and trades: The information content of stock microblogs. European Financial Management, 20(5), 926-957. https://doi.org/10.1111/j.1468-036X.2013.12040.x.
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2), 267-307.
Thelwall, M., Buckley, K., & Paltoglou, G. (2012). Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology, 63(1), 163-173.
Tetlock, P. (2007). Giving content to ınvestor sentiment: The role of media in the stock market. Journal of Finance, 62(3), 1139-1168. https://doi.org/10.1111/j.1540-6261.2007.01232.x.
White, H. (1980). A Heteroskedasticity Consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica: Journal of the Econometric Society, 817-838.
Yıldırım, M., & Yüksel, C. A. (2017). Sosyal medya ile hisse senedi fiyatının günlük hareket yönü arasındaki ilişkinin incelenmesi: Duygu analizi uygulaması. Uluslararası İktisadi ve İdari İncelemeler Dergisi, 22, 33-44. https://doi.org/10.18092/ulikidince.333145.
Zhang, J., & Zhang, C. (2022). Do cryptocurrency markets react to issuer sentiments? Evidence from Twitter. Research in International Business and Finance, 61, 101656.
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