LSTM Based Sentiment Analysis for Cryptocurrency Prediction

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dc.contributor.authorHuang, Xin
dc.contributor.authorZhang, Wenbin
dc.contributor.authorTang, Xuejiao
dc.contributor.authorZhang, Mingli
dc.contributor.authorSurbiryala, Jayachander
dc.contributor.authorIosifidis, Vasileios
dc.contributor.authorLiu, Zhen
dc.contributor.authorZhang, Ji
dc.date.accessioned2021-04-26T17:34:48Z
dc.date.available2021-04-26T17:34:48Z
dc.description.abstractRecent 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.en_US
dc.description.urihttps://www.springerprofessional.de/en/lstm-based-sentiment-analysis-for-cryptocurrency-prediction/19040958en_US
dc.format.extent4 pagesen_US
dc.genrebook chapters preprintsen_US
dc.identifierdoi:10.13016/m2jtaf-hpef
dc.identifier.citationXin Huang et al., LSTM Based Sentiment Analysis for Cryptocurrency Prediction in Database Systems for Advanced Applications, edited by Christian S. Jensen et al., Springer International Publishing, 2021.en_US
dc.identifier.urihttp://hdl.handle.net/11603/21385
dc.language.isoen_USen_US
dc.publisherSpringer Natureen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsAccess to this item will begin on 04/03/2023
dc.titleLSTM Based Sentiment Analysis for Cryptocurrency Predictionen_US
dc.typeTexten_US

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