Forecasting directional bitcoin price returns using aspect-based sentiment analysis on online text data
- Author
- Ekaterina Loginova, Wai Kit Tsang, Guus van Heijningen, Louis-Philippe Kerkhove and Dries Benoit (UGent)
- Organization
- Abstract
- The emergence of cryptocurrency markets has drastically changed how online transactions are conducted and provide a new investment opportunity. This study contributes to the literature on directional cryptocurrency price returns prediction by expanding the set of meaningful features extracted from textual data with sentiment analysis and comparing their usefulness across multiple data sources. In contrast to previous studies, we use fine-grained topic-sentiment features. More specifically, aspect-based sentiment analysis models, JST and TS-LDA, are implemented to incorporate joint topical-sentiment features and the degree of text subjectivity. We collected, and make available, a dataset, which consists of data scraped from Reddit, Bitcointalk and CryptoCompare sources, to demonstrate that proposed features lead to interpretable topics and an improvement in predictive performance.
- Keywords
- Artificial Intelligence, Software, Cryptocurrency, Sentiment analysis, Reddit, Financial forecasting
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8727638
- MLA
- Loginova, Ekaterina, et al. “Forecasting Directional Bitcoin Price Returns Using Aspect-Based Sentiment Analysis on Online Text Data.” MACHINE LEARNING, vol. 113, 2024, pp. 4761–84, doi:10.1007/s10994-021-06095-3.
- APA
- Loginova, E., Tsang, W. K., van Heijningen, G., Kerkhove, L.-P., & Benoit, D. (2024). Forecasting directional bitcoin price returns using aspect-based sentiment analysis on online text data. MACHINE LEARNING, 113, 4761–4784. https://doi.org/10.1007/s10994-021-06095-3
- Chicago author-date
- Loginova, Ekaterina, Wai Kit Tsang, Guus van Heijningen, Louis-Philippe Kerkhove, and Dries Benoit. 2024. “Forecasting Directional Bitcoin Price Returns Using Aspect-Based Sentiment Analysis on Online Text Data.” MACHINE LEARNING 113: 4761–84. https://doi.org/10.1007/s10994-021-06095-3.
- Chicago author-date (all authors)
- Loginova, Ekaterina, Wai Kit Tsang, Guus van Heijningen, Louis-Philippe Kerkhove, and Dries Benoit. 2024. “Forecasting Directional Bitcoin Price Returns Using Aspect-Based Sentiment Analysis on Online Text Data.” MACHINE LEARNING 113: 4761–4784. doi:10.1007/s10994-021-06095-3.
- Vancouver
- 1.Loginova E, Tsang WK, van Heijningen G, Kerkhove L-P, Benoit D. Forecasting directional bitcoin price returns using aspect-based sentiment analysis on online text data. MACHINE LEARNING. 2024;113:4761–84.
- IEEE
- [1]E. Loginova, W. K. Tsang, G. van Heijningen, L.-P. Kerkhove, and D. Benoit, “Forecasting directional bitcoin price returns using aspect-based sentiment analysis on online text data,” MACHINE LEARNING, vol. 113, pp. 4761–4784, 2024.
@article{8727638,
abstract = {{The emergence of cryptocurrency markets has drastically changed how online transactions are conducted and provide a new investment opportunity. This study contributes to the literature on directional cryptocurrency price returns prediction by expanding the set of meaningful features extracted from textual data with sentiment analysis and comparing their usefulness across multiple data sources. In contrast to previous studies, we use fine-grained topic-sentiment features. More specifically, aspect-based sentiment analysis models, JST and TS-LDA, are implemented to incorporate joint topical-sentiment features and the degree of text subjectivity. We collected, and make available, a dataset, which consists of data scraped from Reddit, Bitcointalk and CryptoCompare sources, to demonstrate that proposed features lead to interpretable topics and an improvement in predictive performance.}},
author = {{Loginova, Ekaterina and Tsang, Wai Kit and van Heijningen, Guus and Kerkhove, Louis-Philippe and Benoit, Dries}},
issn = {{0885-6125}},
journal = {{MACHINE LEARNING}},
keywords = {{Artificial Intelligence,Software,Cryptocurrency,Sentiment analysis,Reddit,Financial forecasting}},
language = {{eng}},
pages = {{4761--4784}},
title = {{Forecasting directional bitcoin price returns using aspect-based sentiment analysis on online text data}},
url = {{http://doi.org/10.1007/s10994-021-06095-3}},
volume = {{113}},
year = {{2024}},
}
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