Abstract: Numerous economic, political, and social factors make stock price predictions challenging and unpredictable. This paper focuses on developing an artificial intelligence (AI) model for stock price prediction. The model utilizes LSTM and XGBoost techniques in three sectors: Apple, Google, and Tesla. It aims to detect the impact of combining sentiment analysis with historical data to see how much people's opinions can change the stock market. The proposed model computes sentiment scores using natural language processing (NLP) techniques and combines them with historical data based on Date. The RMSE, R², and MAE metrics are used to evaluate the performance of the proposed model. The integration of sentiment data has demonstrated a significant improvement and achieved a higher accuracy rate compared to historical data alone. This enhances the accuracy of the model and provides investors and the financial sector with valuable information and insights. XGBoost and LSTM demonstrated their effectiveness in stock price prediction; XGBoost outperformed the LSTM technique.

Shimaa Ouf, Mona El Hawary, Amal Aboutabl and Sherif Adel, “A Deep Learning-Based LSTM for Stock Price Prediction Using Twitter Sentiment Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 15(12), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151223

@article{Ouf2024,
title = {A Deep Learning-Based LSTM for Stock Price Prediction Using Twitter Sentiment Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151223},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151223},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {12},
author = {Shimaa Ouf and Mona El Hawary and Amal Aboutabl and Sherif Adel}
}