Paper Title
CRYPTO CURRENCY PRICE PREDICTION USING SENTIMENT ANALYSIS
Article Identifiers
Registration ID: IJNRD_188205
Published ID: IJNRD2303032
DOI: http://doi.one/10.1729/Journal.33411
Authors
Dr. RAJESHWARI D , Dr. Ananthapadmanabha T
Keywords
Cryptocurrency, Bitcoin, LSTM, Bi-Directional LSTM, Twitter data
Abstract
A developing market, crypto currencies are becoming more and more important in the financial sector. The crypto currency market is an ideal research topic because of its low entrance barrier and high data availability. By applying sentiment analysis and machine learning techniques, it is possible to gain insights into the behavior of markets for the difficult work of stock market forecasting. Although there has been some prior research, the majority have only looked at the behavior of Bitcoin. This technique suggests using widely used machine learning tools and readily accessible social media data to forecast changes in the price of the Bitcoin cryptocurrency market. The findings demonstrate that cryptocurrency markets may be predicted using machine learning and sentiment analysis, with Twitter data being able to predict specific cryptocurrencies and outperforming other models. This system presents predictive model for forecasting the Bitcoin price movements using LSTM, Bi-Directional LSTM using sentiment analysis extracted from Twitter data. A review of the relevant literature and related works which this work expands upon is presented, in addition to an analysis of relevant techniques.
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How To Cite (APA)
Dr. RAJESHWARI D & Dr. Ananthapadmanabha T (March-2023). CRYPTO CURRENCY PRICE PREDICTION USING SENTIMENT ANALYSIS . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(3), a310-a326. http://doi.one/10.1729/Journal.33411
Issue
Volume 8 Issue 3, March-2023
Pages : a310-a326
Other Publication Details
Paper Reg. ID: IJNRD_188205
Published Paper Id: IJNRD2303032
Downloads: 000122033
Research Area: Computer Science & TechnologyÂ
Country: Mysuru, Karnataka, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2303032.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2303032
Crossref DOI: http://doi.one/10.1729/Journal.33411
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition


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