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DOI: 10.14569/IJACSA.2024.0151223
PDF

A Deep Learning-Based LSTM for Stock Price Prediction Using Twitter Sentiment Analysis

Author 1: Shimaa Ouf
Author 2: Mona El Hawary
Author 3: Amal Aboutabl
Author 4: Sherif Adel

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 12, 2024.

  • Abstract and Keywords
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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.

Keywords: Sentiment analysis; stocks price prediction; correlation; natural language processing (NLP); machine learning model; LSTM; XGBoost

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}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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