Abstract
This study presents the first multi-platform sentiment analysis of public opinion on the 15-minute city concept across Twitter, Reddit, and news media. Using compressed transformer models and Llama-3-8B for annotation, we classify sentiment across heterogeneous text domains. Our pipeline handles long-form and short-form text, supports consistent annotation, and enables reproducible evaluation. We benchmark five models (DistilRoBERTa, DistilBERT, MiniLM, ELECTRA, TinyBERT) using stratified 5-fold cross-validation, reporting F1-score, AUC, and training time. DistilRoBERTa achieved the highest F1 (0.8292), TinyBERT the best efficiency, and MiniLM the best cross-platform consistency. Results show News data yields inflated performance due to class imbalance, Reddit suffers from summarization loss, and Twitter offers moderate challenge. Compressed models perform competitively, challenging assumptions that larger models are necessary. We identify platform-specific trade-offs and propose directions for scalable, real-world sentiment classification in urban planning discourse.
Abstract (translated)
本研究首次对“15分钟城市”概念在Twitter、Reddit和新闻媒体上的公众意见进行了多平台情感分析。我们使用压缩的Transformer模型和Llama-3-8B进行注释，能够跨异构文本领域分类情感。我们的工作流程处理长格式和短格式文本，支持一致的注释，并使可重复性评估成为可能。通过分层五折交叉验证对五个模型（DistilRoBERTa、DistilBERT、MiniLM、ELECTRA、TinyBERT）进行基准测试，报告F1分数、AUC值和训练时间。其中，DistilRoBERTa取得了最高的F1得分（0.8292），而TinyBERT则在效率方面表现最佳，MiniLM表现出最佳的跨平台一致性。结果表明，新闻数据由于类别不平衡而导致性能虚高，Reddit因总结损失而受到影响，Twitter提供了适度的挑战性。压缩模型的表现竞争激烈，挑战了更大规模模型必不可少的假设。我们识别出各平台特定的权衡，并提出方向以在城市规划讨论中实现可扩展且现实的情感分类。
URL
https://arxiv.org/abs/2509.11443
