AI-Driven Sentiment Analysis of Retail Investor Behavior during Market Volatility: A Study of Twitter Data in Southeast Asia

Sutriani Dewi Sriasih; Farhat Abdul  Razak; Hussein al Ikhsan Ikhsan

doi:10.51903/jmi.v4i1.179

Authors

Sutriani Dewi Sriasih Universitas Muhammadiyah Makassar, Indonesia
Farhat Abdul Razak Universitas Muhammadiyah Makassar, Indonesia
Hussein al Ikhsan Ikhsan Universitas Muhammadiyah Makassar, Indonesia

DOI:

https://doi.org/10.51903/jmi.v4i1.179

Keywords:

AI Sentiment, Retail Investors, Market Volatility, Twitter Data, Stock Index

Abstract

In recent years, retail investor participation in Southeast Asian capital markets has surged, contributing to increased market volatility and making sentiment analysis a critical factor in understanding price dynamics. This study investigates the relationship between social media sentiment and stock market fluctuations by focusing on Twitter data during periods of market volatility in Indonesia, Thailand, and Malaysia. The objective is to examine how collective investor emotions, as expressed through social media, correlate with daily stock index movements. Employing an exploratory quantitative approach, the study integrates Natural Language Processing (NLP) methods, both lexicon-based tools such as VADER and advanced transformer-based models like BERT and GPT, to classify over 150,000 tweets into positive, negative, and neutral sentiments. Sentiment scores were then aggregated and statistically tested using Pearson correlation with daily stock index returns, specifically the IDX Composite, SET Index, and FTSE Bursa Malaysia. The findings reveal a significant negative correlation between negative sentiment and market returns, particularly in the IDX Composite (r = -0.61, p < 0.05), indicating that pessimistic sentiment is associated with market downturns. Thailand’s SET Index and Malaysia’s FTSE Index showed moderate to weak negative correlations, with r = -0.43 and r = -0.27, respectively. These results highlight the sensitivity of emerging markets to emotionally driven retail behavior. The study concludes that AI-based sentiment analysis offers a valuable early warning tool for market volatility and can complement traditional financial indicators. It recommends developing AI-based sentiment dashboards and enhancing digital financial literacy to mitigate emotional reactivity among retail investors.

References

Abimbola, B., de La Cal Marin, E., & Tan, Q. (2024). Enhancing Legal Sentiment Analysis: A Convolutional Neural Network–Long Short-Term Memory Document-Level Model. Machine Learning and Knowledge Extraction, 6(2), 877–897. https://doi.org/10.3390/make6020041

Addo, J. O., Cúg, J., Keelson, S. A., Amoah, J., & Petráková, Z. (2025). Behavioral Risk Management in Investment Strategies: Analyzing Investor Psychology. International Journal of Financial Studies, 13(2), 53. https://doi.org/10.3390/ijfs13020053

Albahli, S., Irtaza, A., Nazir, T., Mehmood, A., Alkhalifah, A., & Albattah, W. (2022). A Machine Learning Method for Prediction of Stock Market Using Real-Time Twitter Data. Electronics, 11(20), 3414. https://doi.org/10.3390/electronics11203414

Alshattnawi, S., Shatnawi, A., AlSobeh, A. M. R., & Magableh, A. A. (2024). Beyond Word-Based Model Embeddings: Contextualized Representations for Enhanced Social Media Spam Detection. Applied Sciences, 14(6), 2254. https://doi.org/10.3390/app14062254

Aziz, M. I. A., Ahmad, N., Zichu, J., & Nor, S. M. (2022). The Impact of COVID-19 on the Connectedness of Stock Index in ASEAN+3 Economies. Mathematics, 10(9), 1417. https://doi.org/10.3390/math10091417

Bello, A., Ng, S. C., & Leung, M. F. (2023). A BERT Framework to Sentiment Analysis of Tweets. Sensors, 23(1), 506. https://doi.org/10.3390/s23010506

Bhattacharya, A., & Sardashti, H. (2022). The Differential Effect of New Product Preannouncements in Driving Institutional and Individual Investor Ownership. Journal of Business Research, 149, 811–823. https://doi.org/10.1016/j.jbusres.2022.05.080

Catelli, R., Pelosi, S., & Esposito, M. (2022). Lexicon-Based vs. Bert-Based Sentiment Analysis: A Comparative Study in Italian. Electronics, 11(3), 374. https://doi.org/10.3390/electronics11030374

Chhajer, P., Shah, M., & Kshirsagar, A. (2022). The Applications of Artificial Neural Networks, Support Vector Machines, and Long–Short Term Memory for Stock Market Prediction. Decision Analytics Journal, 2, 100015. https://doi.org/10.1016/j.dajour.2021.100015

Dumiter, F. C., Turcaș, F., Nicoară, Ștefania A., Bențe, C., & Boiță, M. (2023). The Impact of Sentiment Indices on the Stock Exchange—The Connections between Quantitative Sentiment Indicators, Technical Analysis, and Stock Market. Mathematics, 11(14), 3128. https://doi.org/10.3390/math11143128

Figà-Talamanca, G., & Patacca, M. (2022). An Explorative Analysis of Sentiment Impact on S&P 500 Components Returns, Volatility and Downside Risk. Annals of Operations Research, 342(3), 2095–2117. https://doi.org/10.1007/s10479-022-05129-w

Kirtac, K., & Germano, G. (2024). Sentiment Trading with Large Language Models. Finance Research Letters, 62(PB), 105227. https://doi.org/10.1016/j.frl.2024.105227

Koukaras, P., Nousi, C., & Tjortjis, C. (2022). Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning. Telecom, 3(2), 358–378. https://doi.org/10.3390/telecom3020019

Li, T., Chen, H., Liu, W., Yu, G., & Yu, Y. (2023). Understanding the Role of Social Media Sentiment in Identifying Irrational Herding Behavior in the Stock Market. International Review of Economics & Finance, 87, 163–179. https://doi.org/10.1016/j.iref.2023.04.016

Liapis, C. M., & Kotsiantis, S. (2023). Temporal Convolutional Networks and BERT-Based Multi-Label Emotion Analysis for Financial Forecasting. Information, 14(11), 596. https://doi.org/10.3390/info14110596

Liu, Q., Lee, W. S., Huang, M., & Wu, Q. (2023). Synergy Between Stock Prices and Investor Sentiment in Social Media. Borsa Istanbul Review, 23(1), 76–92. https://doi.org/10.1016/j.bir.2022.09.006

Maqsood, H., Maqsood, M., Yasmin, S., Mehmood, I., Moon, J., & Rho, S. (2022). Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment. Systems, 10(2), 24. https://doi.org/10.3390/systems10020024

Mendoza-Urdiales, R. A., Núñez-Mora, J. A., Santillán-Salgado, R. J., & Valencia-Herrera, H. (2022). Twitter Sentiment Analysis and Influence on Stock Performance Using Transfer Entropy and EGARCH Methods. Entropy, 24(7), 874. https://doi.org/10.3390/e24070874

Oralbekova, D., Mamyrbayev, O., Othman, M., Kassymova, D., & Mukhsina, K. (2023). Contemporary Approaches in Evolving Language Models. Applied Sciences, 13(23), 12901. https://doi.org/10.3390/app132312901

Rezaei, A., Abdellatif, I., & Umar, A. (2025). Towards Economic Sustainability: A Comprehensive Review of Artificial Intelligence and Machine Learning Techniques in Improving the Accuracy of Stock Market Movements. International Journal of Financial Studies, 13(1), 28. https://doi.org/10.3390/ijfs13010028

Sin Huei, N., Zhuang, Z., Toh, M. Y., Ong, T. S., & Teh, B. H. (2022). Exploring Herding Behavior in an Innovative-Oriented Stock Market: Evidence from ChiNext. Journal of Applied Economics, 25(1), 523–542. https://doi.org/10.1080/15140326.2022.2050992

Sinaga, J., Wu, T., & Chen, Y. (2022). Impact of Government Interventions on the Stock Market During COVID-19: A Case Study in Indonesia. In SN Business & Economics (Vol. 2, Issue 9). Springer International Publishing. https://doi.org/10.1007/s43546-022-00312-4

Tran, K. L., Le, H. A., Lieu, C. P., & Nguyen, D. T. (2023). Machine Learning to Forecast Financial Bubbles in Stock Markets: Evidence from Vietnam. International Journal of Financial Studies, 11(4), 133. https://doi.org/10.3390/ijfs11040133

Velu, S. R., Ravi, V., & Tabianan, K. (2023). Multi-Lexicon Classification and Valence-Based Sentiment Analysis as Features for Deep Neural Stock Price Prediction. Sci, 5(1), 8. https://doi.org/10.3390/sci5010008

Yang, S. (2024). Pandemic, Policy, and markets: Insights and Learning from COVID-19’s Impact on Global Stock Behavior. Empirical Economics, 68(2), 555–583. https://doi.org/10.1007/s00181-024-02648-2