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Social Media Data Analysis: Predicting Daily Trends in the Stock Market
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 5 |
| Year of Publication: 2025 |
| Authors: Jasmine K.S., Puvana M.R., Pratheek Nayak |
10.5120/ijca2025924868
|
Jasmine K.S., Puvana M.R., Pratheek Nayak . Social Media Data Analysis: Predicting Daily Trends in the Stock Market. International Journal of Computer Applications. 187, 5 ( May 2025), 35-42. DOI=10.5120/ijca2025924868
Abstract
This study suggests a hybrid stock prediction system that blends sentiment research from social media with time series prediction.In addition to using Twitter sentiment analysis to gauge market sentiment, the system uses Long Short-Term Memory (LSTM) networks and Linear Regression models to forecast past stock values.Test findings show that the use of these complementary approaches together enhances prediction performance, with the LSTM model lowering error significantly relative to traditional forecasting methods. The proposed system generates actionable trading signals (BUY, SELL, HOLD) based on combined analysis, giving investors a comprehensive decision-support tool. The proposed approach improves on the limitations of purely technical or sentiment approaches by creating a stronger forecasting model that includes both quantitative price movements and qualitative mood in the market.
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