- Xin Huang16,
- Wenbin Zhang16,
- Xuejiao Tang17,
- Mingli Zhang18,
- Jayachander Surbiryala19,
- Vasileios Iosifidis17,
- Zhen Liu20 &
- …
- Ji Zhang21
Part of the book series:Lecture Notes in Computer Science ((LNISA,volume 12683))
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Abstract
Recent studies in big data analytics and natural language processing develop automatic techniques in analyzing sentiment in the social media information. In addition, the growing user base of social media and the high volume of posts also provide valuable sentiment information to predict the price fluctuation of the cryptocurrency. This research is directed to predicting the volatile price movement of cryptocurrency by analyzing the sentiment in social media and finding the correlation between them. While previous work has been developed to analyze sentiment in English social media posts, we propose a method to identify the sentiment of the Chinese social media posts from the most popular Chinese social media platform Sina-Weibo. We develop the pipeline to capture Weibo posts, describe the creation of the crypto-specific sentiment dictionary, and propose a long short-term memory (LSTM) based recurrent neural network along with the historical cryptocurrency price movement to predict the price trend for future time frames. The conducted experiments demonstrate the proposed approach outperforms the state of the art auto regressive based model by 18.5% in precision and 15.4% in recall.
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References
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci.2, 1–8 (2011)
Box, G., Jenkins, G., Reinsel, G., Ljung, G.: Time Series Analysis: Forecasting and Control (2016). ISBN 1-118-67502-9. OCLC 915507780
Chen, R., Lazer, M.: Sentiment analysis of twitter feeds for the prediction of stock market movement. Stanford Computer Science 229 (2011)
Hochreiter, S., Schmidhuber, J.: LSTM can solve hard long time lag problems. In: Proceedings of the 9th International Conference on Neural Information Processing Systems, pp. 473–479 (1996)
Hutto, C.J., Gilbert, E.: Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Eighth International AAAI Conference on Weblogs and Social Media, pp. 216–255 (2014)
Joshi, M., Das, D., Gimpel, K., Smith, N.A.: Movie reviews and revenues: an experiment in text regression. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 293–296 (2010)
Pimprikar, R., Ramachadran, S., Senthilkumar, K.: Use of machine learning algorithms and Twitter sentiment analysis for stock market prediction. Int. J. Pure Appl. Math.115, 521–526 (2017)
Wang, H., Can, D., Kazemzadeh, A., Bar, F., Narayanan, S.: Hand gesture recognition using Fourier descriptors. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, pp. 115–120 (2012)
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Authors and Affiliations
University of Maryland, Baltimore County, Baltimore, USA
Xin Huang & Wenbin Zhang
Leibniz University Hannover, Hanover, Germany
Xuejiao Tang & Vasileios Iosifidis
McGill University, Montreal, Canada
Mingli Zhang
University of Stavanger, Stavanger, Norway
Jayachander Surbiryala
Guangdong Pharmaceutical University, Guangzhou, China
Zhen Liu
University of Southern Queensland, Toowoomba, Australia
Ji Zhang
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- Wenbin Zhang
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- Mingli Zhang
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- Vasileios Iosifidis
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- Zhen Liu
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- Ji Zhang
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Correspondence toXin Huang.
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Editors and Affiliations
Aalborg University, Aalborg, Denmark
Christian S. Jensen
Singapore Management University, Singapore, Singapore
Ee-Peng Lim
Academia Sinica, Taipei, Taiwan
De-Nian Yang
The Pennsylvania State University, University Park, PA, USA
Wang-Chien Lee
National Chiao Tung University, Hsinchu, Taiwan
Vincent S. Tseng
Athens University of Economics and Business, Athens, Greece
Vana Kalogeraki
National Cheng Kung University, Tainan City, Taiwan
Jen-Wei Huang
National Tsing Hua University, Hsinchu, Taiwan
Chih-Ya Shen
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Huang, X.et al. (2021). LSTM Based Sentiment Analysis for Cryptocurrency Prediction. In: Jensen, C.S.,et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12683. Springer, Cham. https://doi.org/10.1007/978-3-030-73200-4_47
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