Version 1
: Received: 31 October 2024 / Approved: 1 November 2024 / Online: 1 November 2024 (15:17:24 CET)
How to cite:
Saadatmand, F.; Semyari, R.; Jahanbin, K. Revolutionizing Crypto Forecasts: Deep Learning and Sentiment Synergy for Price Prediction. Preprints2024, 2024110102. https://doi.org/10.20944/preprints202411.0102.v1
Saadatmand, F.; Semyari, R.; Jahanbin, K. Revolutionizing Crypto Forecasts: Deep Learning and Sentiment Synergy for Price Prediction. Preprints 2024, 2024110102. https://doi.org/10.20944/preprints202411.0102.v1
Saadatmand, F.; Semyari, R.; Jahanbin, K. Revolutionizing Crypto Forecasts: Deep Learning and Sentiment Synergy for Price Prediction. Preprints2024, 2024110102. https://doi.org/10.20944/preprints202411.0102.v1
APA Style
Saadatmand, F., Semyari, R., & Jahanbin, K. (2024). Revolutionizing Crypto Forecasts: Deep Learning and Sentiment Synergy for Price Prediction. Preprints. https://doi.org/10.20944/preprints202411.0102.v1
Chicago/Turabian Style
Saadatmand, F., Reza Semyari and Kia Jahanbin. 2024 "Revolutionizing Crypto Forecasts: Deep Learning and Sentiment Synergy for Price Prediction" Preprints. https://doi.org/10.20944/preprints202411.0102.v1
Abstract
Predicting stock and cryptocurrency prices is challenging. One method involves ana-lyzing opinions from individuals and experts. This article focuses on analyzing social media sen-timents for predicting Bitcoin prices using various criteria. In this article, unlike other articles that only used the points obtained from sentiment analysis of tweets, we combined the points of tweets based on other criteria, such as the highest like counts, the highest volume of tweets, and the weighted scores of tweets. In the first stage, we evaluated the deep learning models SL, LG, LGC, BL, and BG without considering sentiment. Ultimately, the BG model performed well in evaluation metrics such as MAE and Loss. In the second stage, we examined the impact of tweet volume on the models' performance. In the third stage, we utilized the simulation results of the VADER model. For the fourth stage, we considered the maximum likes for tweets as a sentiment analysis tool.
Finally, in the last stage, which is the main focus of this article, we employed weighted averaging for sentiment analysis. Model BG provided approximately 11% improvement based on the MAE metric and about 15% improvement based on the Loss metric for better detection the following day.
Keywords
cryptocurrency price; sentiment analysis; deep learning; LSTM; GRU
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
