Classification of Hot News for Financial Forecast Using NLP Techniques

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dc.authoridyildirim, savas/0000-0002-7764-2891|Jothimani, Dhanya/0000-0002-0619-4235|Basar, Ayse/0000-0003-4934-8326
dc.authorwosidyildirim, savas/AAG-4639-2019
dc.authorwosidJothimani, Dhanya/P-9526-2019
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.issued2018
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.descriptionIEEE International Conference on Big Data (Big Data) -- DEC 10-13, 2018 -- Seattle, WAen_US
dc.description.abstractComplex dynamics of stock market could be attributed to various factors ranging from company's financial ratios to investors' sentiment and reaction to Financial news. The paper aims to classify Financial news articles as hot (significant) and non-hot (non-significant). The study is carried out using Dow Jones newswires text feed for a period of four years spanning from 2013 till 2017. Bag-of-ngrams appraoch and Term Frequency-Inverse Document Frequency (TF-IDF) were used for text representation and text weighting, respectively. Four linear classifiers, namely, Logistic Regression (LR), Support Vector Machine (SVM), k Nearest Neighbours (kNN) and multinomial Naive Bayes (mNB) were used. Grid search was used for hyperparameter optimisation. Performance of the classifiers was evaluated using five measures, namely, success rate, precision, recall, F1 measure and area under receiver operating characteristics curve. LR and SVM outperformed other models in terms of all five performance measures for both Bag-of-ngrams model and Bag-of-ngrams model with TF-IDF approach. Use of TF-IDF improved performance of the classifiers, especially, in case of mNB. This study serves as a stepping stone in identification of important/relevant news, which could used as predictors for stock price forecasting.en_US
dc.description.sponsorshipIEEE,IEEE Comp Soc,Expedia Grp,Baidu,Squirrel AI Learning,Ankura,Springeren_US
dc.description.sponsorshipNSERC [CRDPJ-499983-16]; OCE [VIP II 26280]; TMXen_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.endpage4722en_US
dc.identifier.isbn978-1-5386-5035-6
dc.identifier.issn2639-1589
dc.identifier.startpage4719en_US
dc.identifier.urihttps://hdl.handle.net/11411/8527
dc.identifier.wosWOS:000468499304115en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2018 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.subjectFinancial Newsen_US
dc.subjectNatural Language Processingen_US
dc.subjectClassificationen_US
dc.subjectHot Newsen_US
dc.subjectFinancial Forecastsen_US
dc.subjectSentimenten_US
dc.subjectWordsen_US
dc.titleClassification of Hot News for Financial Forecast Using NLP Techniquesen_US
dc.typeConference Objecten_US

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