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http://dx.doi.org/10.6109/jkiice.2022.26.12.1800

Analyzing Effective Poll Prediction Model Using Social Media (SNS) Data Augmentation  

Hwang, Sunik (Datascience, Sungkyunkwan University)
Oh, Hayoung (College of Computing and Informatics, Sungkyunkwan University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.12, 2022 , pp. 1800-1808 More about this Journal
Abstract
During the election period, many polling agencies survey and distribute the approval ratings for each candidate. In the past, public opinion was expressed through the Internet, mobile SNS, or community, although in the past, people had no choice but to survey the approval rating by relying on opinion polls. Therefore, if the public opinion expressed on the Internet is understood through natural language analysis, it is possible to determine the candidate's approval rate as accurately as the result of the opinion poll. Therefore, this paper proposes a method of inferring the approval rate of candidates during the election period by synthesizing the political comments of users through internet community posting data. In order to analyze the approval rate in the post, I would like to suggest a method for generating the model that has the highest correlation with the actual opinion poll by using the KoBert, KcBert, and KoELECTRA models.
Keywords
Opinion polls; social media(SNS); natural language processing; election predictions;
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