Effectiveness of social media in stock market price prediction based on machine learning

Basit Öğe Kaydı
dc.authoridUTKU, Semih/0000-0002-8786-560X
dc.authorwosidUTKU, Semih/T-6314-2018
dc.contributor.authorKaraşahin, Emre
dc.contributor.authorUtku, Semih
dc.contributor.authorÖztürkmenoğlu, Okan
dc.date.accessioned2023-03-22T19:47:17Z
dc.date.available2023-03-22T19:47:17Z
dc.date.issued2022
dc.departmentBelirleneceken_US
dc.description4th International Conference on Intelligent and Fuzzy Systems (INFUS) -- JUL 19-21, 2022 -- Bornova, TURKEYen_US
dc.description.abstractTrying to predict the future using social media data and analytics is very popular today. With this motivation, we aimed to make stock market predictions by creating different analysis models for 10 different banks traded in Borsa Istanbul 100 over 3 different groups that we selected on social media. The groups determined within the scope of the study can be detailed as tweets posted by banks from their accounts, tweets posted with the name of the bank, and tweets with the name of the bank posted from approved accounts. In our analysis, we used various variations, including the tweets' sentiments, replies, retweet and like counts of the tweets, the effects of daily currency (Dollar, Euro, and Gold) prices, and the changes in stock changes up to 3 days. To apply some pre-processing techniques to the collected data, we defined sentiment classes for sentiment analysis, created 6 different models, and analyzed it using 7 different classification algorithms such as Multi-Layer Perceptron, Random Forest, and deep learning algorithm. After all the models and analysis, we got a total of 1440 different results. According to our results, the accuracy rates vary according to the data groups and models we have chosen. The tweet group in which the name of the banks is mentioned can be shown as the most successful data group and we can easily say that there is a certain relation between social media and stock market prices.en_US
dc.identifier.doi10.1007/978-3-031-09176-6_4
dc.identifier.endpage43en_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-s2.0-85135082956en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage36en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-09176-6_4
dc.identifier.urihttps://hdl.handle.net/20.500.14034/584
dc.identifier.volume505en_US
dc.identifier.wosWOS:000889132600004en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer International Publishing Agen_US
dc.relation.journalIntelligent 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.subjectStock marketen_US
dc.subjectClassificationen_US
dc.subjectDeep learningen_US
dc.subjectSocial mediaen_US
dc.subjectSentiment analysisen_US
dc.titleEffectiveness of social media in stock market price prediction based on machine learningen_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Bilgisayar Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu