Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Database Systems for Advanced Applications |
| Untertitel | 26th International Conference, DASFAA 2021, Proceedings |
| Herausgeber/-innen | Christian S. Jensen, Ee-Peng Lim, De-Nian Yang, Wang-Chien Lee, Vincent S. Tseng, Vana Kalogeraki, Jen-Wei Huang, Chih-Ya Shen |
| Erscheinungsort | Cham |
| Herausgeber (Verlag) | Springer Science and Business Media Deutschland GmbH |
| Seiten | 617-621 |
| Seitenumfang | 5 |
| ISBN (elektronisch) | 978-3-030-73200-4 |
| ISBN (Print) | 9783030731991 |
| Publikationsstatus | Veröffentlicht - 6 Apr. 2021 |
| Veranstaltung | 26th International Conference on Database Systems for Advanced Applications, DASFAA 2021 - Taipei, Taiwan Dauer: 11 Apr. 2021 → 14 Apr. 2021 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 12683 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
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.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Database Systems for Advanced Applications: 26th International Conference, DASFAA 2021, Proceedings. Hrsg. / Christian S. Jensen; Ee-Peng Lim; De-Nian Yang; Wang-Chien Lee; Vincent S. Tseng; Vana Kalogeraki; Jen-Wei Huang; Chih-Ya Shen. Cham: Springer Science and Business Media Deutschland GmbH, 2021. S. 617-621 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12683 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - LSTM Based Sentiment Analysis for Cryptocurrency Prediction
AU - Huang, Xin
AU - Zhang, Wenbin
AU - Tang, Xuejiao
AU - Zhang, Mingli
AU - Surbiryala, Jayachander
AU - Iosifidis, Vasileios
AU - Liu, Zhen
AU - Zhang, Ji
PY - 2021/4/6
Y1 - 2021/4/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85104727052&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-73200-4_47
DO - 10.1007/978-3-030-73200-4_47
M3 - Conference contribution
AN - SCOPUS:85104727052
SN - 9783030731991
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 617
EP - 621
BT - Database Systems for Advanced Applications
A2 - Jensen, Christian S.
A2 - Lim, Ee-Peng
A2 - Yang, De-Nian
A2 - Lee, Wang-Chien
A2 - Tseng, Vincent S.
A2 - Kalogeraki, Vana
A2 - Huang, Jen-Wei
A2 - Shen, Chih-Ya
PB - Springer Science and Business Media Deutschland GmbH
CY - Cham
T2 - 26th International Conference on Database Systems for Advanced Applications, DASFAA 2021
Y2 - 11 April 2021 through 14 April 2021
ER -