Predicting the Price Direction of Bitcoin Using Twitter Data and Machine Learning

Bitcoin is a decentralized digital currency that was intro- duced in 2009 and since then, has become increasingly popular as one of the most known and highly valued currencies. Contributing factors to its rise include crypto Twitter influencers. An engaged audience on Twitter seems to have an influe...

Full description

Saved in:
Bibliographic Details
Published in:2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA) pp. 46 - 52
Main Authors: Kanji, Abdul Mannan, Chaudhary, Ishita, Shankar, Rithika Lakshmi, Srinivasa, Gowri
Format: Conference Proceeding
Language:English
Published: IEEE 28.10.2022
Subjects:
Bitcoin
Blogs
Candlestick plots
Computational modeling
Cryptocurrency
Machine learning models
Moon
Online banking
Price direction prediction
Social networking (online)
Support vector machine classification
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract Bitcoin is a decentralized digital currency that was intro- duced in 2009 and since then, has become increasingly popular as one of the most known and highly valued currencies. Contributing factors to its rise include crypto Twitter influencers. An engaged audience on Twitter seems to have an influence on the cryptocurrency market. In this paper, we analyze the impact that tweets have on the price of Bitcoin. Using word-clouds and candlestick plots, we gain insight into the factors that affect Bitcoin prices. We also use various machine learning techniques to automatically classify the sentiment in Tweets related to cryptocurrencies. We incorporate these and other relevant features to build and compare the performance of multiple machine learning models to predict the direction (increase or decrease) of the price of Bitcoin.
AbstractList Bitcoin is a decentralized digital currency that was intro- duced in 2009 and since then, has become increasingly popular as one of the most known and highly valued currencies. Contributing factors to its rise include crypto Twitter influencers. An engaged audience on Twitter seems to have an influence on the cryptocurrency market. In this paper, we analyze the impact that tweets have on the price of Bitcoin. Using word-clouds and candlestick plots, we gain insight into the factors that affect Bitcoin prices. We also use various machine learning techniques to automatically classify the sentiment in Tweets related to cryptocurrencies. We incorporate these and other relevant features to build and compare the performance of multiple machine learning models to predict the direction (increase or decrease) of the price of Bitcoin.
Author Kanji, Abdul Mannan
Srinivasa, Gowri
Shankar, Rithika Lakshmi
Chaudhary, Ishita
Author_xml – sequence: 1
  givenname: Abdul Mannan
  surname: Kanji
  fullname: Kanji, Abdul Mannan
  email: kanjimannan@gmail.com
  organization: PES University,PES Center for Pattern Recognition and Dept. of Computer Science and Engineering,Bengaluru,India
– sequence: 2
  givenname: Ishita
  surname: Chaudhary
  fullname: Chaudhary, Ishita
  email: ishita1001@gmail.com
  organization: PES University,PES Center for Pattern Recognition and Dept. of Computer Science and Engineering,Bengaluru,India
– sequence: 3
  givenname: Rithika Lakshmi
  surname: Shankar
  fullname: Shankar, Rithika Lakshmi
  email: shankar.rithika@gmail.com
  organization: PES University,PES Center for Pattern Recognition and Dept. of Computer Science and Engineering,Bengaluru,India
– sequence: 4
  givenname: Gowri
  surname: Srinivasa
  fullname: Srinivasa, Gowri
  email: gsrinivasa@pes.edu
  organization: PES University,PES Center for Pattern Recognition and Dept. of Computer Science and Engineering,Bengaluru,India
BookMark eNotj8tKAzEYRiPoQmufwE1eYMbcL8s646UwYsV2XdLMH_uDZiQNiG9vxa4OfBw-OFfkPE8ZCKGctZwzf7vs-rduoY0wqhVMiNZ757R2Z2TurePGaGUFY_qSvK4KjBgr5nda90BXBSPQHgsctynTKdE7rHHCTDeHP2n9jbVCoX2ogYY80ucQ95iBDhBKPhrX5CKFjwPMT5yRzcP9untqhpfHZbcYGuTc1UYIY3fGqVHBCNY4SDww7ZiySQoPzqWgpNScych5jHIMcmcBuLUCEktJzsjN_y8CwPar4GcoP9tTqPwFy_ZNvA
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/ICDSCA56264.2022.9988558
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
Accès Toulouse INP et ENVT - IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 9781665472005
1665472006
EndPage 52
ExternalDocumentID 9988558
Genre orig-research
GroupedDBID 6IE
6IL
CBEJK
RIE
RIL
ID FETCH-LOGICAL-i118t-2267b684d4ede768ef1a058047f329e88fa4335103c11cc3da3b7ee1772ef0ff3
IEDL.DBID RIE
IngestDate Thu Jan 18 11:14:31 EST 2024
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i118t-2267b684d4ede768ef1a058047f329e88fa4335103c11cc3da3b7ee1772ef0ff3
PageCount 7
ParticipantIDs ieee_primary_9988558
PublicationCentury 2000
PublicationDate 2022-Oct.-28
PublicationDateYYYYMMDD 2022-10-28
PublicationDate_xml – month: 10
  year: 2022
  text: 2022-Oct.-28
  day: 28
PublicationDecade 2020
PublicationTitle 2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA)
PublicationTitleAbbrev ICDSCA
PublicationYear 2022
Publisher IEEE
Publisher_xml – name: IEEE
Score 1.818733
Snippet Bitcoin is a decentralized digital currency that was intro- duced in 2009 and since then, has become increasingly popular as one of the most known and highly...
SourceID ieee
SourceType Publisher
StartPage 46
SubjectTerms Bitcoin
Blogs
Candlestick plots
Computational modeling
Cryptocurrency
Machine learning models
Moon
Online banking
Price direction prediction
Social networking (online)
Support vector machine classification
Title Predicting the Price Direction of Bitcoin Using Twitter Data and Machine Learning
URI https://ieeexplore.ieee.org/document/9988558
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NSwMxEB3a4sGTSit-k4NHt91NtpvkqK1FD5aKFXor2WQie9mVdqt_3yRdK4IXbyEEAhOYeW_yZgbg2lDKuYk9ctOOoNjMRtL5xEgazSVKx4lyEYZN8OlULBZy1oKbXS0MIgbxGfb9Mvzlm0pvfKps4KiBGA5FG9qc822t1rc4J5aDx9H4ZXTr4nnmcyWU9pvjv-amhLAxOfjfhYfQ-6m_I7NdZDmCFpZdeJ6t_KeKlykTh9pIGNhOGpdVlaSy5K6odVWUJOgAyPyz8KU6ZKxqRVRpyFMQTiJpeqq-9eB1cj8fPUTNQISocDygjhxU4nkmUpOiQccT0CYqHoo45ZZRiUJYlTLme-TpJNGaGcVyjpg4BI02tpYdQ6esSjwB4oiWUI4b65x7RuVQC8NU6CzNqcEstqfQ9eZYvm97XiwbS5z9vX0O-97i3qdTcQGderXBS9jTH3WxXl2Fh_oCtU-VDw
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fS8MwED7mFPRJZRN_mwcf7damaZM-6nRsuI2JE_Y22uQifWlldvrvm2R1IvjiWwiEwAXuvu_y3R3AtaKUc-Vb5CYNQdGx9hLjE71ESZ5gYjhRJtywCT6ZiPk8mTbgZlMLg4hOfIYdu3R_-aqUK5sq6xpqIKJIbMF2xBgN1tVa3_IcP-kOe_fPvVsT0WObLaG0Ux_4NTnFBY7-_v-uPID2TwUemW5iyyE0sGjB03Rpv1WsUJkY3EbcyHZSO62yIKUmd3kly7wgTglAZp-5LdYh92mVkrRQZOykk0jqrqqvbXjpP8x6A68eieDlhglUngFLPIsFUwwVGqaAOkj9SPiM65AmKIROWRjaLnkyCKQMVRpmHDEwGBq1r3V4BM2iLPAYiKFaIjXsWGbcciqDW0JkQsYsowpjX59Ay5pj8bbuerGoLXH69_YV7A5m49FiNJw8nsGetb718FScQ7NarvACduRHlb8vL92jfQGmRphW
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2022+IEEE+2nd+International+Conference+on+Data+Science+and+Computer+Application+%28ICDSCA%29&rft.atitle=Predicting+the+Price+Direction+of+Bitcoin+Using+Twitter+Data+and+Machine+Learning&rft.au=Kanji%2C+Abdul+Mannan&rft.au=Chaudhary%2C+Ishita&rft.au=Shankar%2C+Rithika+Lakshmi&rft.au=Srinivasa%2C+Gowri&rft.date=2022-10-28&rft.pub=IEEE&rft.spage=46&rft.epage=52&rft_id=info:doi/10.1109%2FICDSCA56264.2022.9988558&rft.externalDocID=9988558