Sentiment Analysis of Elon Musk's Twitter Data Using LSTM and ANFIS-SVM

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dc.contributor.authorErkartal, Bugra
dc.contributor.authorYilmaz, Atinc
dc.date.accessioned2024-03-13T10:30:21Z
dc.date.available2024-03-13T10:30:21Z
dc.date.issued2022
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description4th International Conference on Intelligent and Fuzzy Systems (INFUS) -- JUL 19-21, 2022 -- Bornova, TURKEYen_US
dc.description.abstractSocial media plays a huge role spreading words to millions and influencing their opinions. Twitter is one of the most essential platform that reach over 300 million active users and 500 million tweets per day, it plays a significant role spreading the word around the world,. These tweets covers a various subjects from personal conversations to globally important topics such as updates about Covidl9 and macroeconomic subjects. Especially in financial matters, it is a very common situation that business owners, even politicians report the news on Twitter first. The Tesla's and SpaceX's CEO and owner Elon Musk's tweets had a huge impact on coin market or even stock exchanges. Although many accused him of market manipulation his tweets impact cannot be underestimated. In 2020 and 2021 there are various tweets that strike the stock market instantly both in the positive and negative direction. This study aims to predict the direction of his tweets and perform a sentiment analysis using both Long-Short Term Memory (LSTM) and Adaptive Neuro Fuzzy Interface Systems (ANFIS)-SVM(Support Vector Machines) models. The dataset is obtained by using Twitter API which spans a time horizon of 5 years. In order to compare the results under same conditions same preprocessing steps are performed for both models. According to the results, LSTM performs a superior performance with its 72.2% accuracy against ANFIS-SVM model with 74.1%.en_US
dc.identifier.doi10.1007/978-3-031-09176-6_70
dc.identifier.endpage635en_US
dc.identifier.isbn978-3-031-09176-6
dc.identifier.isbn978-3-031-09175-9
dc.identifier.issn2367-3370
dc.identifier.issn2367-3389
dc.identifier.scopus2-s2.0-85135061177
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage626en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-09176-6_70
dc.identifier.urihttps://hdl.handle.net/20.500.12662/3303
dc.identifier.volume505en_US
dc.identifier.wosWOS:000889132600070
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherSpringer International Publishing Agen_US
dc.relation.ispartofIntelligent And Fuzzy Systems: Digital Acceleration And The New Normal, Infus 2022, Vol 2en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectANFISen_US
dc.subjectLSTMen_US
dc.subjectElon musken_US
dc.titleSentiment Analysis of Elon Musk's Twitter Data Using LSTM and ANFIS-SVMen_US
dc.typeConference Objecten_US

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