Measuring Online Public Opinion for Decision Making: Application of Deep Learning on Political Context

Daesik Kim, Chung Joo Chung and Kihong Eom
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Daesik Kim: Department of Political Science and Diplomacy, Kyungpook National University, Daegu 41566, Korea
Chung Joo Chung: Department of Media and Communication, Kyungpook National University, Daegu 41566, Korea
Kihong Eom: Department of Political Science and Diplomacy, Kyungpook National University, Daegu 41566, Korea

Sustainability, 2022, vol. 14, issue 7, 1-16

Abstract: Thoughts travel faster and farther through cyberspace where people interact with one another regardless of limitations in language, space, and time. Is a poll sufficient to measure people’s opinions in this era of hyperconnectivity? This study introduces a deep learning method to measure online public opinion. By analyzing Korean texts from Twitter, this study generates time-series data on online sentiment toward the South Korean president, comparing it to traditional presidential approval to demonstrate the independence of the masses’ online discourse. The study tests different algorithms and deploys the model with high accuracy and advancement. The analysis suggests that online public opinion represents a unique population as opposed to offline surveys. The study model examines Korean texts generated by online users and automatically predicts their sentiments, which translate into group attitudes by aggregation. The research method can extend to other studies, including those on environmental and cultural issues, which have greater online presence. This provides opportunities to examine the influences of social phenomenon, benefiting individuals seeking to understand people in an online context. Moreover, it helps scholars in analyzing those public opinions—online or offline—that are more important in their decision making to assess the practicality of the methods.

Keywords: online public opinion; offline public opinion; polling; deep learning; BERT (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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