Dynamic Topic Modelling for Cryptocurrency Community Forums

Author:

Linton M.,Teo E. G. S.,Bommes E.,Chen C. Y.,Härdle Wolfgang Karl

Publisher

Springer Berlin Heidelberg

Reference18 articles.

1. Bao, Y., & Datta, A. (2014). Simultaneously discovering and quantifying risk types from textual risk disclosures. Management Science, 60(6), 1371–1391.

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

3. Blei, D., & Lafferty, J. (2006). Dynamic topic models. In Proceedings of the 23rd international conference on Machine learning (AMC).

4. Bommes, E., Chen, C. Y., Härdle, W. K. (2017). Textual sentiment and sector-specific reaction. Forthcoming.

5. Chang, J., Boyd-Graber, J. L., Wang, C., & Blei, D. M. (2009). Reading tea leaves: How humans interpret topic models. Advances in Neural Information Processing Systems, 288–296.

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