Forecasting Ethereum Prices with Machine Learning, Deep Learning, and Explainable Artificial Intelligence Using Multi-source Market Articles and Hybrid Sentiment Analysis

Satish, Naresh Kumar, Mercadier, Mathieu, Muntean, Cristina Hava and Simiscuka, Anderson Augusto (2025) Forecasting Ethereum Prices with Machine Learning, Deep Learning, and Explainable Artificial Intelligence Using Multi-source Market Articles and Hybrid Sentiment Analysis. In: Deep Learning Theory and Applications. DeLTA 2025. Communications in Computer and Information Science (2627). Springer, Cham, Bilbao, pp. 184-203. ISBN 978-303204338-2

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Abstract

The cryptocurrency market is widely regarded as one of the most volatile financial markets due to inconsistencies in its pricing factors. Despite this volatility, it continues to attract a large population of investors, many of whom incur significant losses. To address this challenge and support risk assessment for investors, users, and other stakeholders, this paper focuses on forecasting Ethereum prices by analyzing social media sentiment. The study gathers data from sources such as global news headlines and Reddit discussion forums, enhancing it with hybrid sentiment features derived from the VADER, BERT and TextBlob models. These sentiment insights are then correlated with Ethereums financial parameters to establish meaningful relationships within the data, which are used to train machine learning models. The study evaluates the predictive performance of Random Forest, Extreme Gradient Boosting, and Long Short-Term Memory models. Among these, Extreme Gradient Boosting demonstrated superior performance, effectively capturing complex relationships within the data and achieving an R-squared value of 0.982115. To further enhance the studys risk assessment capabilities, the concept of Explainable Artificial Intelligence (XAI) is employed to improve transparency and accountability in the model outcomes. Specifically, Shapley Additive Explanations (SHAP) are used to interpret the feature interactions within the Extreme Gradient Boosting model, thereby increasing its reliability and providing deeper insights into its decision-making process.

Item Type:	Book Section
Uncontrolled Keywords:	Discussion forum; Gradient boosting; Large population; Machine-learning; Multi-Sources; Pricing factors; Risks assessments; Sentiment analysis; Sentiment features; Social media
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing H Social Sciences > HG Finance > Money > Digital currency > Cryptocurrencies Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Tamara Malone
Date Deposited:	06 Dec 2025 16:14
Last Modified:	06 Dec 2025 16:14
URI:	https://norma.ncirl.ie/id/eprint/9010

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