在伊朗以色列美国冲突中使用联邦变换器模型进行隐私保护的专题情感分析
Privacy Preserving Topic-wise Sentiment Analysis of the Iran Israel USA Conflict Using Federated Transformer Models
AI总结：
本文通过联邦学习和深度学习技术，分析伊朗以色列美国冲突的用户评论情感，采用ELECTRA模型获得91.32%准确率，并在隐私保护下保持89.59%的性能。
摘要：
2026年伊朗以色列美国冲突的升级引发了全球社交媒体的广泛讨论。随着人们越来越多地使用这些平台表达观点，分析这些讨论中的公众情绪可以提供有价值的全球公众看法洞察。本研究旨在通过挖掘YouTube新闻频道的用户生成评论来分析全球公众对伊朗以色列美国冲突的情绪。该工作通过结合专题情感分析与现代深度学习技术及联邦学习，为公众意见分析做出了贡献。为此，收集了约19,000条YouTube评论，并预处理以去除噪声和标准化文本。初始情感标签使用VADER情感分析器生成，随后通过人工检查验证以提高可靠性。潜在狄利克雷分配（LDA）被应用于识别与冲突相关的关键讨论主题。几种基于变换器的模型，包括BERT、RoBERTa、XLNet、DistilBERT、ModernBERT和ELECTRA，被微调用于情感分类。表现最佳的模型进一步整合到联邦学习环境中，以在保护用户数据隐私的同时实现分布式训练。此外，使用SHAP的可解释人工智能（XAI）技术应用于解释模型预测并识别影响情感分类的关键词汇。实验结果表明，变换器模型表现有效，其中ELECTRA在情感分类中取得了91.32%的准确率。联邦学习在保护隐私的同时也保持了强大的性能，在两个客户端配置中达到89.59%的准确率。
英文摘要：
The recent escalation of the Iran Israel USA conflict in 2026 has triggered widespread global discussions across social media platforms. As people increasingly use these platforms for expressing opinions, analyzing public sentiment from these discussions can provide valuable insights into global public perception. This study aims to analyze global public sentiment regarding the Iran Israel USA conflict by mining user-generated comments from YouTube news channels. The work contributes to public opinion analysis by introducing a privacy preserving framework that combines topic wise sentiment analysis with modern deep learning techniques and Federated Learning. To achieve this, approximately 19,000 YouTube comments were collected from major international news channels and preprocessed to remove noise and normalize text. Sentiment labels were initially generated using the VADER sentiment analyzer and later validated through manual inspection to improve reliability. Latent Dirichlet Allocation (LDA) was applied to identify key discussion topics related to the conflict. Several transformer-based models, including BERT, RoBERTa, XLNet, DistilBERT, ModernBERT, and ELECTRA, were fine tuned for sentiment classification. The best-performing model was further integrated into a federated learning environment to enable distributed training by preserving user data privacy. Additionally, Explainable Artificial Intelligence (XAI) techniques using SHAP were applied to interpret model predictions and identify influential words affecting sentiment classification. Experimental results demonstrate that transformer models perform effectively, and among them, ELECTRA achieved the best performance with 91.32% accuracy. The federated learning also maintained strong performance while preserving privacy, achieving 89.59% accuracy in a two client configuration.
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