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LSTM Based Sentiment Analysis for Cryptocurrency Prediction

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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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Author information

Authors and Affiliations

  1. University of Maryland, Baltimore County, Baltimore, USA

    Xin Huang & Wenbin Zhang

  2. Leibniz University Hannover, Hanover, Germany

    Xuejiao Tang & Vasileios Iosifidis

  3. McGill University, Montreal, Canada

    Mingli Zhang

  4. University of Stavanger, Stavanger, Norway

    Jayachander Surbiryala

  5. Guangdong Pharmaceutical University, Guangzhou, China

    Zhen Liu

  6. University of Southern Queensland, Toowoomba, Australia

    Ji Zhang

Authors
  1. Xin Huang

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  2. Wenbin Zhang

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  3. Xuejiao Tang

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  4. Mingli Zhang

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  5. Jayachander Surbiryala

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  6. Vasileios Iosifidis

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  7. Zhen Liu

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  8. Ji Zhang

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Corresponding author

Correspondence toXin Huang.

Editor information

Editors and Affiliations

  1. Aalborg University, Aalborg, Denmark

    Christian S. Jensen

  2. Singapore Management University, Singapore, Singapore

    Ee-Peng Lim

  3. Academia Sinica, Taipei, Taiwan

    De-Nian Yang

  4. The Pennsylvania State University, University Park, PA, USA

    Wang-Chien Lee

  5. National Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

  6. Athens University of Economics and Business, Athens, Greece

    Vana Kalogeraki

  7. National Cheng Kung University, Tainan City, Taiwan

    Jen-Wei Huang

  8. 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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Chapter
JPY 3498
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eBook
JPY 14871
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
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Softcover Book
JPY 18589
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
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Tax calculation will be finalised at checkout

Purchases are for personal use only


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