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dc.contributor.advisorÖzdamar, İbrahim Özgür
dc.contributor.authorTaylan, Enes
dc.date.accessioned2017-05-24T11:45:44Z
dc.date.available2017-05-24T11:45:44Z
dc.date.copyright2017-05
dc.date.issued2017-05
dc.date.submitted2017-05-23
dc.identifier.urihttp://hdl.handle.net/11693/32994
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of International Relations, İhsan Doğramacı Bilkent University, 2017.en_US
dc.descriptionIncludes bibliographical references (leaves 104-108).en_US
dc.description.abstractTo advance social science in the direction of accurate and reliable quantitative models, especially in the fields of International Relations and Political Science, new novel methodologies borrowed from the Computer Science and Statistics should be employed. In International Relations, quantitative analysis can be carried out to understand foreign policy topic relations in public discourse of decision makers. In domestic politics, Election Forecasting is a suitable area, because of its offering of already quantified vote results and its importance in the decision-making process in domestic politics and foreign policy. This work embarks upon a computational statistical model built on social media (Twitter) data and texts’ meaning extracted, analyzed and modeled with the state-of-the-art methodologies from Computer Science (Machine Learning and Natural Language Processing) and statistics to forecast election results and foreign policy orientation. To verify the model, Turkish General Election 2015, US Presidential Election 2016 and campaign period of Donald Trump are analyzed. This work shows that, sentiment of political tweets can be captured with high predictive accuracy (92% in Turkish, 96% in English) and using opinion poll results for a given period of time, vote percentage fluctuations can be predicted. Furthermore, it is possible to capture the foreign policy orientation of a candidate by his and his team’s tweets in the campaign period.en_US
dc.description.statementofresponsibilityby Enes Taylan.en_US
dc.format.extentxv, 108 leaves : charts ; 30 cmen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectElection Forecastingen_US
dc.subjectForeign Policy Analysisen_US
dc.subjectMachine Learningen_US
dc.subjectNatural Language Processingen_US
dc.subjectSentiment Analysisen_US
dc.titleNumbers in politics : comparative quantitative analysis & modeling in foreign policy orientation and election forecastingen_US
dc.title.alternativePolitikada sayılar: dış politika yönelimi ve seçim tahmininde karşılaştırmalı sayısal analiz ve modellemeen_US
dc.typeThesisen_US
dc.departmentDepartment of International Relationsen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB155527


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