Building Domain-Specific Lexicons: An Application to Financial News

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dc.authorscopusid23096618000
dc.authorscopusid56544707000
dc.authorscopusid24334948400
dc.authorscopusid21742123700
dc.contributor.authorYildirim, S.
dc.contributor.authorJothimani, D.
dc.contributor.authorKavaklio?lu, C.
dc.contributor.authorBener, A.
dc.date.accessioned2024-07-18T20:17:03Z
dc.date.available2024-07-18T20:17:03Z
dc.date.issued2019
dc.description2019 International Conference on Deep Learning and Machine Learning in Emerging Applications, Deep-ML 2019 -- 26 August 2019 through 28 August 2019 -- -- 153122en_US
dc.description.abstractNatural Language Processing (NLP) has gained attention in the recent years. Previous research (such as WordNet and Cyc) has focused on developing an all purpose (generalised) polarised lexicons. However, these lexicons do not provide much information in different domains such as Finance and Medical Sciences. Using these lexicons for text classification could affect the prediction accuracy. Therefore, there is a need for building domain- and context-specific lexicons. To achieve this, in this work, a label based propagation based word embedding algorithm has been proposed to obtain positive and negative lexicons. The proposed algorithm works on the principle of graph theory and word embedding. The proposed algorithm is tested on Dow Jones news wires text feed to classify the Financial news as hot and non-hot. Three classifiers, namely, Logistic Regression, Random Forest and XGBoost, employing polarised lexicons, seed words and random words were used. The performance of classifiers in all cases was evaluated using accuracy. Lexicons generated using the proposed approach were effective in classifying the Financial news articles as hot and non-hot compared to classifiers using seed words and random words. Proposed label propagation with word embedding algorithm generates context-specific lexicons, which aids in helps in better representation of text in natural processing tasks and avoids the problem of dimensionality. © 2019 IEEE.en_US
dc.description.sponsorshipCRDPJ-499983-16, OCE VIP II 26280en_US
dc.description.sponsorshipThis research is supported in part by the following grants: NSERC CRDPJ-499983-16; OCE VIP II 26280; and TMX.en_US
dc.identifier.doi10.1109/Deep-ML.2019.00013
dc.identifier.endpage26en_US
dc.identifier.isbn9781728129143
dc.identifier.scopus2-s2.0-85074878604en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage23en_US
dc.identifier.urihttps://doi.org/10.1109/Deep-ML.2019.00013
dc.identifier.urihttps://hdl.handle.net/11411/6394
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications, Deep-ML 2019en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassificationen_US
dc.subjectDow Jones Dataseten_US
dc.subjectFinancial Lexiconsen_US
dc.subjectFinancial Newsen_US
dc.subjectLabel Propagationen_US
dc.subjectMachine Learningen_US
dc.subjectClassification (Of İnformation)en_US
dc.subjectDecision Treesen_US
dc.subjectEmbeddingsen_US
dc.subjectFinanceen_US
dc.subjectGraph Theoryen_US
dc.subjectLearning Algorithmsen_US
dc.subjectLearning Systemsen_US
dc.subjectMachine Learningen_US
dc.subjectNatural Language Processing Systemsen_US
dc.subjectText Processingen_US
dc.subjectDow Jonesen_US
dc.subjectEmbedding Algorithmsen_US
dc.subjectFinancial Lexiconsen_US
dc.subjectFinancial Newsen_US
dc.subjectLabel Propagationen_US
dc.subjectLogistic Regressionsen_US
dc.subjectNatural Language Processingen_US
dc.subjectPerformance Of Classifieren_US
dc.subjectDeep Learningen_US
dc.titleBuilding Domain-Specific Lexicons: An Application to Financial Newsen_US
dc.typeConference Objecten_US

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