The predictive power of social media in cryptocurrencies value fluctuation - A sentiment analysis approach on "memecoins"

Detalhes bibliográficos
Autor(a) principal: Guerra, João Filipe Vestia
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/150904
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
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spelling The predictive power of social media in cryptocurrencies value fluctuation - A sentiment analysis approach on "memecoins"CryptocurrencyTwitterGoogle TrendsSentiment analysisMachine learningDissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceSince the emergence of Bitcoin in 2009, cryptocurrencies have been an increasingly spoken asset, becoming a global phenomenon. Since Bitcoin, thousands of other coins have been introduced to the public with their value depending on the perception of the market. The high volatility as well as the lack of regulation makes these assets a risky investment, however, the amount of people who believe that cryptocurrencies represent the future of money has never been higher. The reason behind the high volatility can be understood because cryptocurrencies prices depend on factors like technological progress, regulations and legal matters, security issues, political factors, among others that obviously include supply and demand. This high volatility associated with the rising interest on the assets is driving the need of understanding and predicting the impacts that certain factors may have on the value of cryptocurrencies. This research addresses the ability of using data from social media platforms, specifically Twitter to predict changes in memecoins like Dogecoin, by using learning algorithms.Neto, Miguel de Castro Simões FerreiraSarmento, Pedro Alexandre ReisRUNGuerra, João Filipe Vestia2024-01-27T01:32:04Z2023-01-272023-01-27T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/150904TID:203250478enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:33:19Zoai:run.unl.pt:10362/150904Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:21.709201Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv The predictive power of social media in cryptocurrencies value fluctuation - A sentiment analysis approach on "memecoins"
title The predictive power of social media in cryptocurrencies value fluctuation - A sentiment analysis approach on "memecoins"
spellingShingle The predictive power of social media in cryptocurrencies value fluctuation - A sentiment analysis approach on "memecoins"
Guerra, João Filipe Vestia
Cryptocurrency
Twitter
Google Trends
Sentiment analysis
Machine learning
title_short The predictive power of social media in cryptocurrencies value fluctuation - A sentiment analysis approach on "memecoins"
title_full The predictive power of social media in cryptocurrencies value fluctuation - A sentiment analysis approach on "memecoins"
title_fullStr The predictive power of social media in cryptocurrencies value fluctuation - A sentiment analysis approach on "memecoins"
title_full_unstemmed The predictive power of social media in cryptocurrencies value fluctuation - A sentiment analysis approach on "memecoins"
title_sort The predictive power of social media in cryptocurrencies value fluctuation - A sentiment analysis approach on "memecoins"
author Guerra, João Filipe Vestia
author_facet Guerra, João Filipe Vestia
author_role author
dc.contributor.none.fl_str_mv Neto, Miguel de Castro Simões Ferreira
Sarmento, Pedro Alexandre Reis
RUN
dc.contributor.author.fl_str_mv Guerra, João Filipe Vestia
dc.subject.por.fl_str_mv Cryptocurrency
Twitter
Google Trends
Sentiment analysis
Machine learning
topic Cryptocurrency
Twitter
Google Trends
Sentiment analysis
Machine learning
description Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
publishDate 2023
dc.date.none.fl_str_mv 2023-01-27
2023-01-27T00:00:00Z
2024-01-27T01:32:04Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/150904
TID:203250478
url http://hdl.handle.net/10362/150904
identifier_str_mv TID:203250478
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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