STOCK MARKET PREDICTION AND SENTIMENT ANALYSIS
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Department of Computer Engineering, Smt. Kashibai Navale College of Engineering, Pune, India
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Abstract
The increasing role of social media in financial discussions has created a need to understand how public sentiment can influence stock-related decision-making. This paper presents a Sentiment-Driven Stock Market Simulator, a web-based platform that demonstrates the relationship between user-generated tweets and simulated stock price movement. The system allows registered users to post stock-related tweets, which are processed through an external AI-based sentiment analysis service. The returned sentiment score is combined with engagement factors such as likes and comments to calculate an impact score. This impact score influences the simulated stock price, while a rule-based prediction engine generates a predicted price for comparison. The application uses React, Node.js, Express.js, MongoDB, Socket.IO, and Recharts to support full-stack development, database storage, real-time updates, and live chart visualization. The system also uses Mean Absolute Error to compare the actual simulated price with the predicted price. The proposed work is intended as an educational and demonstrative FinTech platform rather than a real-world trading or investment prediction tool.
Keywords
Sentiment Analysis; Stock Market Simulation; Real-Time Systems; Socket.IO; FinTech; Rule-Based Prediction
Article Information
PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 6
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Jun 27 2026
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Bachhav, V. A., Sabnis, A. A., Dhabale, P. V. & Ambhore, A. R. (2026). Stock Market Prediction and Sentiment Analysis. International Journal of Science, Strategic Management and Technology, 02(6). https://doi.org/10.55041/ijsmt.v2i6.204
Bachhav, Vilas, et al.. "Stock Market Prediction and Sentiment Analysis." International Journal of Science, Strategic Management and Technology, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i6.204.
Bachhav, Vilas,Aditya Sabnis,Piyush Dhabale, and Akshay Ambhore. "Stock Market Prediction and Sentiment Analysis." International Journal of Science, Strategic Management and Technology 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i6.204.
References
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[2] H. Mao, S. Counts, and J. Bollen, “Predicting financial markets: Comparing survey, news, Twitter and search engine data,” arXiv preprint arXiv:1112.1051, 2011.
[3] R. P. Schumaker and H. Chen, “Textual analysis of stock market prediction using breaking financial news: The AZFinText system,” ACM Transactions on Information Systems, vol. 27, no. 2, Article 12, 2009.
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[5] G. Ranco, D. Aleksovski, G. Caldarelli, M. Grčar, and I. Mozetič, “The effects of Twitter sentiment on stock price returns,” PLoS ONE, vol. 10, no. 9, e0138441, 2015.
[6] T. O. Sprenger, A. Tumasjan, P. G. Sandner, and I. M. Welpe, “Tweets and trades: The information content of stock microblogs,” European Financial Management, vol. 20, no. 5, pp. 926-957, 2014.
[7] B. Liu, Sentiment Analysis and Opinion Mining. San Rafael, CA, USA: Morgan & Claypool Publishers, 2012.
[8] C. J. Willmott and K. Matsuura, “Advantages of the mean absolute error over the root mean square error in assessing average model performance,” Climate Research, vol. 30, no. 1, pp. 79-82, 2005.
[9] Express.js, “Express.js Documentation: Node.js Web Application Framework,” OpenJS Foundation. [Online]. Available: https://expressjs.com/
[10] Socket.IO, “Socket.IO Documentation,” Socket.IO. [Online]. Available: https://socket.io/docs/
[11] MongoDB, “MongoDB Manual,” MongoDB Inc. [Online]. Available: https://www.mongodb.com/docs/
[12] Recharts, “Recharts Documentation,” Recharts. [Online]. Available: https://recharts.org/
[2] H. Mao, S. Counts, and J. Bollen, “Predicting financial markets: Comparing survey, news, Twitter and search engine data,” arXiv preprint arXiv:1112.1051, 2011.
[3] R. P. Schumaker and H. Chen, “Textual analysis of stock market prediction using breaking financial news: The AZFinText system,” ACM Transactions on Information Systems, vol. 27, no. 2, Article 12, 2009.
[4] V. S. Pagolu, K. N. R. Challa, G. Panda, and B. Majhi, “Sentiment analysis of Twitter data for predicting stock market movements,” arXiv preprint arXiv:1610.09225, 2016.
[5] G. Ranco, D. Aleksovski, G. Caldarelli, M. Grčar, and I. Mozetič, “The effects of Twitter sentiment on stock price returns,” PLoS ONE, vol. 10, no. 9, e0138441, 2015.
[6] T. O. Sprenger, A. Tumasjan, P. G. Sandner, and I. M. Welpe, “Tweets and trades: The information content of stock microblogs,” European Financial Management, vol. 20, no. 5, pp. 926-957, 2014.
[7] B. Liu, Sentiment Analysis and Opinion Mining. San Rafael, CA, USA: Morgan & Claypool Publishers, 2012.
[8] C. J. Willmott and K. Matsuura, “Advantages of the mean absolute error over the root mean square error in assessing average model performance,” Climate Research, vol. 30, no. 1, pp. 79-82, 2005.
[9] Express.js, “Express.js Documentation: Node.js Web Application Framework,” OpenJS Foundation. [Online]. Available: https://expressjs.com/
[10] Socket.IO, “Socket.IO Documentation,” Socket.IO. [Online]. Available: https://socket.io/docs/
[11] MongoDB, “MongoDB Manual,” MongoDB Inc. [Online]. Available: https://www.mongodb.com/docs/
[12] Recharts, “Recharts Documentation,” Recharts. [Online]. Available: https://recharts.org/
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