The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies
Open accessAbstract
This paper proposes a method to predict fluctuations in the prices of cryptocurrencies, which are increasingly used for online transactions worldwide. Little research has been conducted on predicting fluctuations in the price and number of transactions of a variety of cryptocurrencies. Moreover, the few methods proposed to predict fluctuation in currency prices are inefficient because they fail to take into account the differences in attributes between real currencies and cryptocurrencies. This paper analyzes user comments in online cryptocurrency communities to predict fluctuations in the prices of cryptocurrencies and the number of transactions. By focusing on three cryptocurrencies, each with a large market size and user base, this paper attempts to predict such fluctuations by using a simple and efficient method.
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Cites
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Opinion mining and sentiment analysis
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VADER: a parsimonious rule-based model for sentiment analysis of social media text
Hutto, C., & Gilbert, E. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14550 · Cited by 6,087
Twitter mood predicts the stock market
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Cited by
Bitcoin price prediction using machine learning: An approach to sample dimension engineering
Chen, Z., Li, C., & Sun, W. (2020) · Journal of Computational and Applied Mathematics · doi:10.1016/j.cam.2019.112395 · Cited by 420
Mallqui, D. C. A., & Fernandes, R. A. S. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2018.11.038 · Cited by 253
Charting the Landscape of Online Cryptocurrency Manipulation
Nizzoli, L., Tardelli, S., Avvenuti, M., Cresci, S., Tesconi, M., & Ferrara, E. (2020) · IEEE Access · doi:10.1109/access.2020.3003370 · Cited by 132
Cloud-Based Sentiment Analysis for Interactive Agents
Keijsers, M., Bartneck, C., & Kazmi, H. S. (2019) · Proceedings of the 7th International Conference on Human-Agent Interaction · doi:10.1145/3349537.3351883 · Cited by 9
Metadata
- Authors
- Kim, Y. B., Kim, J. G., Kim, W., Im, J. H., Kim, T. H., Kang, S. J., & Kim, C. H.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2016-08-17
- Volume
- 11
- Issue
- 8
- Pages
- e0161197
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2513607014
- Cited by
- 286
- References
- 49
- FWCI
- 33.57
- Type
- journal article
- Language
- en
- Open access
- gold
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- Kim, Y. B., Kim, J. G., Kim, W., Im, J. H., Kim, T. H., Kang, S. J., & Kim, C. H. (2016). Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies. PLOS ONE. https://doi.org/10.1371/journal.pone.0161197
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
