Evaluation of Citizen Opinion Extraction Across Cities

Abstract

Gathering citizen feedback, analyzing it, and comparing the results against other cities is essential for improving government policy and service quality. However, because different cities have different policies and services, the opinions of citizens in different cities also differ. This makes it difficult to analyze citizen opinions adapted for multiple cities using machine learning. In this study, we propose a method for extracting citizen opinions across cities. We evaluated our proposed method using a tweet dataset collected from citizens of Yokohama, Sapporo, and Sendai, confirming its effectiveness to fine-tune a model using the source city and re-fine-tune it with a few tweets from the target city. We clarified that training data in the target city can be effectively selected using the model trained with tweets from the source city, with high confidence in the prediction.