Forecasting directional bitcoin price returns using aspect-based sentiment analysis on online text data

Author:

Loginova EkaterinaORCID,Tsang Wai KitORCID,van Heijningen Guus,Kerkhove Louis-Philippe,Benoit Dries F.ORCID

Funder

Bijzonder Onderzoeksfonds

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference70 articles.

1. Abraham, J., Higdon, D., Nelson, J., & Ibarra, J. (2018). Cryptocurrency price prediction using tweet volumes and sentiment analysis. SMU Data Science Review, 1(3), 1.

2. Alessandretti, L., ElBahrawy, A., Aiello, L. M., & Baronchelli, A. (2018). Machine learning the cryptocurrency market. arXiv preprint, arXiv:180508550.

3. Amjad, M., & Shah, D. (2017). Trading bitcoin and online time series prediction. In NIPS 2016 time series workshop (pp. 1–15).

4. Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2003). Modeling and forecasting realized volatility. Econometrica, 71(2), 579–625.

5. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.

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