# Mapping Japan’s China-related twitter discourse (2010–2024) using BERTopic

**Authors:** Shanshan Zhang, Xi Chen

PMC · DOI: 10.1371/journal.pone.0343085 · PLOS One · 2026-02-18

## TL;DR

This study uses AI to analyze over a million Japanese tweets about China from 2010 to 2024, revealing trends in public opinion and sentiment.

## Contribution

The study introduces a novel method combining BERTopic and sentiment analysis to explore evolving public discourse on China in Japan.

## Key findings

- Public attention to China has grown, focusing on diplomacy, environment, economy, and culture.
- Sentiment toward China is mostly negative, though some topics show neutral or positive attitudes.
- Topic-sentiment analysis shows public opinion shifts in response to both events and topic characteristics.

## Abstract

Given China’s profound influence on Japan, Japanese public opinion toward China has been the focus of debate. Yet little is known about how such perceptions evolve over time within the digital public sphere. Drawing on a dataset of over one million China-related tweets from Japan (2010–2024), this study integrates BERTopic topic modeling with large language model–driven sentiment analysis to trace the dynamic evolution of Japanese perceptions of China. Empirical analyses find that: (1) Public attention to China has steadily increased, concentrating on four domains: diplomacy & security, environment & health, economy & trade,and culture & society; (2) overall sentiment is predominantly negative, with neutral and positive attitudes appearing in specific topics; and (3) topic–sentiment linkage analysis reveals divergent affective tendencies across topics, indicating that public opinion evolves in response not only to external events but also to the inherent characteristics of topics. By applying computational analysis to large-scale social media data, this study uncovers the dynamic structure of Japanese public opinion regarding China, offering insights into the mechanisms of opinion formation with implications for Sino-Japanese relations. Methodologically, it contributes innovative approaches to the analysis of transnational public discourse.

## Full-text entities

- **Genes:** NFIC (nuclear factor I C) [NCBI Gene 4782] {aka CTF, CTF5, NF-I, NF-I/C, NF1-C, NFI}
- **Diseases:** COVID-19 (MESH:D000086382), Health (OMIM:603663), avian influenza (MESH:D005585), anxiety (MESH:D001007), flooding (MESH:C565009)
- **Species:** H7N9 subtype (serotype) [taxon 333278], Homo sapiens (human, species) [taxon 9606], Helianthus annuus (common sunflower, species) [taxon 4232]

## Full text

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## Figures

8 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12915976/full.md

## References

62 references — full list in the complete paper: https://tomesphere.com/paper/PMC12915976/full.md

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Source: https://tomesphere.com/paper/PMC12915976