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dc.contributor.authorKilimci, Zeynep Hilal
dc.date.accessioned2021-06-14T20:25:04Z
dc.date.available2021-06-14T20:25:04Z
dc.date.issued2020
dc.identifier.issn1300-1884
dc.identifier.issn1304-4915
dc.identifier.urihttps://doi.org/10.17341/gazimmfd.501551
dc.identifier.urihttps://hdl.handle.net/11376/3678
dc.descriptionKilimci, Zeynep Hilal/0000-0003-1497-305Xen_US
dc.description.abstractThe stock market forecasting is popular research topic for analysts. In this study, it is proposed to estimate direction of Bist100 index by financial sentiment analysis. To our knowledge, this is the first study in literature using Twitter for forecasting stock market direction and doing this with deep ensemble models. The contributions of study are fourfold: First, feature set is enriched semantically to eliminate size limitation problem in Twitter. In first stage, meaningful features that express dataset are selected by means of information gain and ant colony optimization. Next, features are enriched in meaning, context, syntax using document representation models such as Avg(Word2vec), Avg(Glove), Avg(Word2vec)+Avg(Glove), TF-IDF+Avg(Word2vec), TF-IDF+Avg(Glove). Secondly, it is proposed to improve system performance performing classification with multiple learning algorithms. Instead of traditional classification algorithms, a deep ensemble model (DTM) is constructed blending deep learning architectures such as Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory Networks. Third, majority voting and stacking methods are used to obtain final decision of deep ensemble model. Fourthly, Turkish and English Twitter datasets are employed to demonstrate that proposed approach improves classification performance. Consequently, experimental results show that proposed model is significantly superior to previous studies when compared with literature studies.en_US
dc.language.isoturen_US
dc.publisherGazi Univ, Fac Engineering Architectureen_US
dc.identifier.doi10.17341/gazimmfd.501551en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectStock market predictionen_US
dc.subjectdeep ensemble modelen_US
dc.subjectsentiment analysisen_US
dc.titleFinancial sentiment analysis with Deep Ensemble Models (DEMs) for stock market predictionen_US
dc.typearticleen_US
dc.relation.journalJournal Of The Faculty Of Engineering And Architecture Of Gazi Universityen_US
dc.department[0-Belirlenecek]en_US
dc.identifier.volume35en_US
dc.identifier.issue2en_US
dc.identifier.startpage635en_US
dc.identifier.endpage650en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.institutionauthor[0-Belirlenecek]
dc.department-temp[Kilimci, Zeynep Hilal] Dogus Univ, Dept Comp Engn, TR-34722 Istanbul, Turkey; [Kilimci, Zeynep Hilal] Kocaeli Univ, Dept Informat Syst Engn, TR-41001 Kocaeli, Turkeyen_US
dc.identifier.wosWOS:000520599400007en_US


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