Topic Modeling and Sentiment Analysis on Japanese Online Media's Coverage of Nuclear Energy

TL;DR

This study analyzes Japanese online media content and comments on nuclear energy using topic modeling and sentiment analysis to understand public opinion and discourse shifts post-Fukushima.

Contribution

It introduces a novel approach combining topic modeling, sentiment analysis with large language models, and network analysis on social media data related to nuclear energy in Japan.

Findings

Identified main topics in online discussions about nuclear energy.

Classified public sentiment towards these topics.

Detected discourse shifts during water release events in 2023.

Abstract

Thirteen years after the Fukushima Daiichi nuclear power plant accident, Japan's nuclear energy accounts for only approximately 6% of electricity production, as most nuclear plants remain shut down. To revitalize the nuclear industry and achieve sustainable development goals, effective communication with Japanese citizens, grounded in an accurate understanding of public sentiment, is of paramount importance. While nationwide surveys have traditionally been used to gauge public views, the rise of social media in recent years has provided a promising new avenue for understanding public sentiment. To explore domestic sentiment on nuclear energy-related issues expressed online, we analyzed the content and comments of over 3,000 YouTube videos covering topics related to nuclear energy. Topic modeling was used to extract the main topics from the videos, and sentiment analysis with large language models classified user sentiments towards each topic. Additionally, word co-occurrence network analysis was performed to examine the shift in online discussions during August and September 2023 regarding the release of treated water. Overall, our results provide valuable insights into the online discourse on nuclear energy and contribute to a more comprehensive understanding of public sentiment in Japan.