Investor Sentiment-driven Stock Price Prediction Using Optimized Deep Learning Models
Year: 2025
Type: article
Abstract: The technique of estimating the future value of a company's shares, or any other financial instrument listed on an exchange, is known as shares market prediction. Investors face a significant challenge when attempting to forecast future events in the stock market. Investors will aim to maximize their profits if they can accurately predict a company's future price. Social media users' opinions are having a greater impact on the performance of the stock market. To create a prediction model, this study examines a variety of prediction techniques. According to the approach, actions should be taken in two stages. Sentiment analysis and historical data are used in the first stage. The second stage places a strong emphasis on deep learning. A helpful technique for comprehending the tone of comments on social media platforms is sentiment analysis. Understanding how emotions impact stock prices is crucial. Using the Deep Learning module, we create a forecast model based on correlation. The outcomes demonstrated that the suggested method regularly produced more accurate forecasts.
Author: INTERNATIONAL JOURNAL OF ADVANCED RESEARCH & INNOVATIONS
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W6893513450
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