Machine Learning Models for Political Video Advertisement Classification Banerjee, Boudhayan
dc.contributor.department Computer Science
dc.contributor.majorProfessor Adisak Sukul
dc.contributor.majorProfessor Wallapak Tavanapong 2019-12-02T19:52:12.000 2020-06-30T01:34:28Z 2020-06-30T01:34:28Z Sun Jan 01 00:00:00 UTC 2017 2017-01-01
dc.description.abstract <p>Investment in online political ad marketing is gaining traction very rapidly. In the United States, the 2016 presidential election campaign witnessed a substantial increase in political advertisement expenditure on online platforms like YouTube. Therefore, political researchers are interested in analyzing trends of political ads in an online medium. But currently, there is no existing method or application that can classify political advertisement from a large dataset of online ads. In this paper, we attempted to solve this problem by proposing a model that can automatically classify political video advertisements using machine learning algorithms such as Support Vector Machine, Linear Regression, and Naïve Bayes classifier. We will also focus on feature engineering for this classification problem. We applied text features and non-text features like color and facial features for classification purposes. We trained 3 different models with a different feature sets and compare results among them. We also created an ensemble with these 3 models and achieved an F1-score of 0.97.</p>
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dc.identifier archive/
dc.identifier.articleid 1443
dc.identifier.contextkey 15882419
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath creativecomponents/365
dc.source.bitstream archive/|||Fri Jan 14 23:48:07 UTC 2022
dc.subject.disciplines Artificial Intelligence and Robotics
dc.subject.keywords political advertisement classification
dc.subject.keywords machine learning
dc.subject.keywords feature generation
dc.subject.keywords OCR
dc.subject.keywords text classification
dc.title Machine Learning Models for Political Video Advertisement Classification
dc.type article
dc.type.genre creativecomponent
dspace.entity.type Publication
relation.isOrgUnitOfPublication f7be4eb9-d1d0-4081-859b-b15cee251456 Computer Science creativecomponent
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