Deep Learning Approaches for Sentiment Analysis on Financial Microblog Dataset

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dc.authoridBasar, Ayse/0000-0003-4934-8326
dc.authorwosidBasar, Ayse/ABF-9265-2020
dc.contributor.authorYildirim, Savas
dc.contributor.authorJothimani, Dhanya
dc.contributor.authorKavaklioglu, Can
dc.contributor.authorBasar, Ayse
dc.date.accessioned2024-07-18T20:52:10Z
dc.date.available2024-07-18T20:52:10Z
dc.date.issued2019
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.descriptionIEEE International Conference on Big Data (Big Data) -- DEC 09-12, 2019 -- Los Angeles, CAen_US
dc.description.abstractSentiment analysis of financial news and social media messages along with movement of stock prices could aid in improving the forecasting accuracy of stock prices. In this regard, we aim to perform sentiment analysis of a financial microblog, namely, StockTwits. We carried out the analysis on labelled messages of twelve stocks for a period of five months ranging from May 2019 to September 2019 using various Deep Learning (DL) approaches. We compared the performance of the DL classifiers with traditional machine learning approaches. Long Short Term Memory (LSTM) model and its variations such as bidirectional LSTM and bidrirectional LSTM with dropout outperformed other classifiers. Though use of dropout mechanism did not improve the performance of the model but there was a decrease in bias and variance. Further, we evaluated the performance of various optimizers such as rmsprop, adam, adagrad, adamax and nadam on LSTM. The success rate of all optimizers was similar.en_US
dc.description.sponsorshipIEEE Comp Soc,IEEE,Baidu,Very,Ankuraen_US
dc.description.sponsorshipNSERC [CRDPJ-499983-16]; TMX; OCE VIP II [26280]en_US
dc.description.sponsorshipThis research is supported in part by the following grants: NSERC CRDPJ-499983-16; OCE VIP II 26280; and TMXen_US
dc.identifier.endpage5584en_US
dc.identifier.isbn978-1-7281-0858-2
dc.identifier.issn2639-1589
dc.identifier.scopus2-s2.0-85081338092en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage5581en_US
dc.identifier.urihttps://hdl.handle.net/11411/8534
dc.identifier.wosWOS:000554828705092en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2019 Ieee International Conference on Big Data (Big Data)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectStocktwitsen_US
dc.subjectNatural Language Processingen_US
dc.subjectClassificationen_US
dc.subjectStocktwitsen_US
dc.subjectDeep Learningen_US
dc.subjectFinancial Microblogen_US
dc.titleDeep Learning Approaches for Sentiment Analysis on Financial Microblog Dataseten_US
dc.typeConference Objecten_US

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