Extracting Citizen Feedback from Social Media by Appraisal Opinion Type Viewpoint

Abstract

Citizen feedback is essential for improving hospitality in government policies and customer services. In this study, we propose a method for extracting citizen feedback from social media according to appraisal opinion type by filtering tweets based on multiple viewpoints such as regional dependency, status of citizen, and polarity. To improve the F1-score of the estimation of opinion unit viewpoints, we implement a multitask learning framework to estimate associated viewpoints using a BERT model. In the experiment, we focus on two domains of citizen life during the COVID-19 pandemic: nursery school life and restaurant takeout services. Our multitask learning approach was effective in estimating viewpoints on opinions. In addition, we demonstrate that citizen feedback filtering based on specific viewpoints is valuable in investigating chronological opinion transitions by appraisal opinion types.