Trust Beyond Numbers: Data Augmentation Formula for Poll Prediction

Abstract

During election periods, many polling agencies survey and distribute approval ratings for each candidate. In the past, public opinion was expressed through the Internet, mobile SNS, or the community, historically, individuals had limited options for gauging approval ratings and primarily relied on traditional opinion polls. Analyzing public opinion expressed on the Internet through natural language analysis allows for determining a candidate's approval rate with comparable accuracy to traditional opinion polls. Therefore, this paper proposes a method of inferring the approval rates of candidates during election periods by synthesizing the political comments of users through internet community posting data. To analyze the approval ratings of the posts, we propose to generate a model that has the highest correlation with the actual polls using data augmentation techniques, using the KcBert, KoBert, and KoELECTRA models.