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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM63632

Stock Forecasting with Social Media Insights

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63632

Volume:

10 |

5 |

May 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

CG

Chinmayi GS

Master of computer applications

Bapuji institute of engineering and technology

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Mentors

VpGL

Vinay patel G L

Master of computer applications

Bapuji institute of engineering and technology

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Abstract

The stock market environment itself is highly complicated and dynamic, and there are several reasons for the fluctuation of the stock market, including price history, events in the world, and views shared via social media websites. However, the emotional and psychological aspects that affect the investor's decision have been left out of traditional methods that only consider quantifiable aspects. The proposed stock market predictor based on social media sentiment analysis would address this. issue using deep learning algorithms and Natural Language Processing. The use of this project involves social. media analysis of sentiments at sites like Twitter in combination with past data about the stock by applying LSTM, for. time series prediction and sentiment analysis using software such as NLTK. The developed web-based framework is. designed in a dual role manner that enables the authorized users to predict the stock values and obtain analytical reports. whereas the administrator takes care of the users and updates the FAQs section. An extensive decision-making system has. been provided to the users from novice to professional. investors by merging the quantitative and qualitative factors. The use of deep learning and sentiment analysis. increases the reliability and efficiency of the predictions helping the investors in taking accurate decisions. Hence, through integrating. traditional market analysis and. social media information, the proposed project is an innovative step towards financial prediction.

KEYWORDS

Stock Forecasting

Cite This Article

Chinmayi GS, Vinay patel G L, "Stock Forecasting with Social Media Insights," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-5, May 2026.

Full Article/Conclusion

Thus, one can emphasize that the Stock Forecasting Using Social Media Insights system is a great solution that applies ideas related to financial sentiment analysis and machine learning to create an intelligent forecast mechanism. As a result of the combination of historical data about prices of stocks and real-time sentiments extracted from tweets, the application will contribute greatly to improving the prediction process for the user and gaining understanding of the situation in the market. The user module will make the prediction process easier for the users, while the admin module will help in the management of the whole system

References

1.         Forecasting stock market volatility using social media sentiment analysis, Christina Saravanos, Andreas Kanavos Neural Computing and Applications, Volume 37, Pages 10771–10794, 2025, DOI: [10.1007/s00521-024-10807- w](https://doi.org/10.1007/s00521-024-10807-w), eISSN: 1433-3058. 2.         Predicting stock price movement using social       network conversation-level features, W. Li Neurocomputing,  olume 473, Pages              1–12,           2025 DOI:[10.1016/j.neucom.2025.02.039] (https://doi.org/10.1016/j.neucom.2025.02.039), ISSN: 0925-2312 3.         GNN-based social media sentiment analysis for stock market prediction, P. Zhang Applied Soft Computing, Volume 121, Article 108708, 2025, DOI: [10.1016/j.asoc.2022.108708] (https://doi.org/10.1016/j.asoc 2022.108708), ISSN: 1568- 4946   4.         Prediction of stock market using sentiment analysis and deep reinforcement learning, A.Y. Chaudhari, Procedia Computer Science, Volume 190, Pages 108–115, 2025, DOI:[10.1016/j.procs.2025.02.014] https://doi.org/10.1016/j.procs 2025 02.014), ISSN: 2215- 0161   5.         Enhancing Stock Price Forecasting with Social Media Text, B. Li, Proceedings of the 2024 International Conference on Artificial Intelligence and Applications (AIAP 2024), Pages 133–141,2025.

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Publication Date

5/21/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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