Tweet sentiment analysis for cryptocurrencies

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dc.authorid0000-0002-8649-6013
dc.contributor.authorŞaşmaz, Emreen_US
dc.contributor.authorTek, Faik Borayen_US
dc.date.accessioned2022-05-20T13:44:56Z
dc.date.available2022-05-20T13:44:56Z
dc.date.issued2021-10-13
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Computer Engineeringen_US
dc.description.abstractMany traders believe in and use Twitter tweets to guide their daily cryptocurrency trading. In this project, we investigated the feasibility of automated sentiment analysis for cryptocurrencies. For the study, we targeted one cryptocurrency (NEO) altcoin and collected related data. The data collection and cleaning were essential components of the study. First, the last five years of daily tweets with NEO hashtags were obtained from Twitter. The collected tweets were then filtered to contain or mention only NEO. We manually tagged a subset of the tweets with positive, negative, and neutral sentiment labels. We trained and tested a Random Forest classifier on the labeled data where the test set accuracy reached 77%. In the second phase of the study, we investigated whether the daily sentiment of the tweets was correlated with the NEO price. We found positive correlations between the number of tweets and the daily prices, and between the prices of different crypto coins. We share the data publicly.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationŞaşmaz, E. & Tek, F. B. (2021). Tweet sentiment analysis for cryptocurrencies. 2021 6th International Conference on Computer Science and Engineering (UBMK), 613-618. doi:10.1109/UBMK52708.2021.9558914en_US
dc.identifier.doi10.1109/UBMK52708.2021.9558914
dc.identifier.endpage618
dc.identifier.isbn9781665429085
dc.identifier.isbn9781665429078
dc.identifier.isbn9781665429092
dc.identifier.issn2521-1641
dc.identifier.issn2768-0592
dc.identifier.scopus2-s2.0-85125868484
dc.identifier.scopusqualityN/A
dc.identifier.startpage613
dc.identifier.urihttps://hdl.handle.net/11729/4345
dc.identifier.urihttp://dx.doi.org/10.1109/UBMK52708.2021.9558914
dc.indekslendigikaynakScopusen_US
dc.institutionauthorŞaşmaz, Emreen_US
dc.institutionauthorTek, Faik Borayen_US
dc.institutionauthorid0000-0002-8649-6013
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherIEEEen_US
dc.relation.ispartof2021 6th International Conference on Computer Science and Engineering (UBMK)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBERTen_US
dc.subjectCryptocurrenciesen_US
dc.subjectRandom forest algorithmen_US
dc.subjectSentiment analysisen_US
dc.subjectClassification (of information)en_US
dc.subjectCostsen_US
dc.subjectElectronic moneyen_US
dc.subjectRandom forestsen_US
dc.subjectSocial networking (online)en_US
dc.subjectCryptocurrencyen_US
dc.subjectData cleaningen_US
dc.subjectData collectionen_US
dc.subjectHashtagsen_US
dc.subjectLabeled dataen_US
dc.subjectPositive/negativeen_US
dc.subjectRandom forest classifieren_US
dc.subjectDecision treesen_US
dc.titleTweet sentiment analysis for cryptocurrenciesen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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