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dc.contributor.authorSert, Onur Can
dc.contributor.authorŞahin, Salih Doruk
dc.contributor.authorÖzyer, Tansel
dc.contributor.authorAlhajj, Reda
dc.date.accessioned2020-01-22T06:37:41Z
dc.date.available2020-01-22T06:37:41Z
dc.date.issued2020en_US
dc.identifier.citationSert, O. C., Şahin S. D., Özyer, T. ve Alhajj, R. (2020). Analysis and prediction in sparse and high dimensional text data: The case of Dow Jones stock market. Physica A: Statistical Mechanics and its Applications, 545. https://doi.org/10.1016/j.physa.2019.123752.en_US
dc.identifier.issn03784371
dc.identifier.urihttps://doi.org/10.1016/j.physa.2019.123752
dc.identifier.urihttps://hdl.handle.net/20.500.12511/4911
dc.description.abstractIn this research, we proposed a text analysis system to predict stock market movements using news and social media data. It is a scalable prediction system for sparse and high dimensional feature sets. Using the developed system, we collected 12,560 articles from New York Times covering one year time period, and 2,854,333 tweets from Twitter covering 4 months time period. We analysed the collected data using entity extraction, sentiment analysis and topic modelling techniques. We applied our feature set creation and elastic net regression based training method. The analyses have been used to train different prediction models. Using these trained prediction models, we predicted stock market movements for Dow Jones Index and showed that the proposed method can make promising predictions. In different sets of experiments, highly accurate (up to 70.90% accuracy) predictions are made by the proposed approach. These predicted values also correlated (up to 0.2315 correlation coefficient value) with real Dow Jones Index values. Further, we report performance comparison results for various prediction models that we trained with different set of features to analyse the importance of time interval and feature space size. Our test results show that it is possible to make reasonable stock movement prediction by integrating news and related social media data, analysing them using named entity extraction, sentiment analysis and topic modelling techniques together with prediction models which use features that are created from these analysis results.en_US
dc.language.isoengen_US
dc.publisherElsevier B.V.en_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectNamed Entity Recognitionen_US
dc.subjectTopic Modellingen_US
dc.subjectSentiment Analysisen_US
dc.subjectSocial Network Analysisen_US
dc.subjectStock Market Movement Predictionen_US
dc.subjectMsaeneen_US
dc.titleAnalysis and prediction in sparse and high dimensional text data: The case of Dow Jones stock marketen_US
dc.typearticleen_US
dc.relation.ispartofPhysica A: Statistical Mechanics and its Applicationsen_US
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.authorid0000-0001-6657-9738en_US
dc.identifier.volume545en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1016/j.physa.2019.123752en_US
dc.identifier.wosqualityQ2en_US
dc.identifier.scopusqualityQ2en_US


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