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DOI: 10.14569/IJACSA.2022.01310105
PDF

Cryptocurrency Price Prediction using Forecasting and Sentiment Analysis

Author 1: Shaimaa Alghamdi
Author 2: Sara Alqethami
Author 3: Tahani Alsubait
Author 4: Hosam Alhakami

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 10, 2022.

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Abstract: In recent years, many investors have used cryptocurrencies, prompting specialists to find out the factors that affect cryptocurrencies’ prices. Therefore, one of the most popular methods that have been used to predict cryptocurrency prices is sentiment analysis. It is a widespread technique utilized by many researchers on social media platforms, particularly on Twitter. Thus, to determine the relationship between investors’ sentiment and the volatility of cryptocurrency prices, this study forecasts the cryptocurrency prices using the Long-Term-Short-Memory (LSTM) deep learning algorithm. In addition, Twitter users’ sentiments using Support Vector Machine (SVM) and Naive Bayes (NB) machine learning approaches are analyzed. As a result, in the classification of the bitcoin (BTC) and Ethereum (ETH) datasets of investors’ sentiments into (Positive, Negative, and Neutral), the SVM algorithm outperformed the NB algorithm with an accuracy of 93.95% and 95.59%, respectively. Furthermore, the forecasting regression model achieves an error rate of 0.2545 for MAE, 0.2528 for MSE, and 0.5028 for RMSE.

Keywords: Sentiment analysis; cryptocurrencies; forecasting; bitcoin; ethereum

Shaimaa Alghamdi, Sara Alqethami, Tahani Alsubait and Hosam Alhakami, “Cryptocurrency Price Prediction using Forecasting and Sentiment Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 13(10), 2022. http://dx.doi.org/10.14569/IJACSA.2022.01310105

@article{Alghamdi2022,
title = {Cryptocurrency Price Prediction using Forecasting and Sentiment Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.01310105},
url = {http://dx.doi.org/10.14569/IJACSA.2022.01310105},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {10},
author = {Shaimaa Alghamdi and Sara Alqethami and Tahani Alsubait and Hosam Alhakami}
}



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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