低资源土耳其语的情感识别：在TREMO上微调BERTurk并在排外政治话语上测试
Emotion Recognition for Low-Resource Turkish: Fine-Tuning BERTurk on TREMO and Testing on Xenophobic Political Discourse
AI总结：
本研究针对低资源土耳其语，基于BERTurk和TREMO数据集构建准确率达92.62%的情感识别模型，分析土耳其社交媒体上的反难民情绪话语，为本地化NLP工具及相关领域应用提供支撑。
摘要：
X（原Twitter）等社交媒体平台在塑造公共话语和社会规范方面发挥着关键作用。本研究考察了土耳其社交媒体上的“Sessiz Istila（无声入侵）”一词，揭示了叙利亚难民涌入背景下反难民情绪的上升趋势。我们利用BERTurk和TREMO数据集，开发了一款专为土耳其语定制的先进情感识别模型（Emotion Recognition Model，ERM），在对快乐、恐惧、愤怒、悲伤、厌恶和惊讶等情绪进行分类时，准确率达到92.62%。通过将该模型应用于大规模X平台数据，本研究揭示了土耳其话语中的情感细微差别，通过推进代表性不足语言的情感分析、加深我们对全球数字话语及土耳其语独特语言挑战的理解，为计算社会科学做出了贡献。研究结果强调了本地化自然语言处理工具的变革潜力，我们的ERM模型可在土耳其语语境下的实时情感分析中提供实际应用。通过覆盖营销、公共关系和危机管理等关键领域，这些模型能够通过及时准确的情感追踪助力改善决策制定，凸显了推进兼顾区域与语言细微差别的研究的重要意义。
英文摘要：
Social media platforms like X (formerly Twitter) play a crucial role in shaping public discourse and societal norms. This study examines the term Sessiz Istila (Silent Invasion) on Turkish social media, highlighting the rise of anti-refugee sentiment amidst the Syrian refugee influx. Using BERTurk and the TREMO dataset, we developed an advanced Emotion Recognition Model (ERM) tailored for Turkish, achieving 92.62% accuracy in categorizing emotions such as happiness, fear, anger, sadness, disgust, and surprise. By applying this model to large-scale X data, the study uncovers emotional nuances in Turkish discourse, contributing to computational social science by advancing sentiment analysis in underrepresented languages and enhancing our understanding of global digital discourse and the unique linguistic challenges of Turkish. The findings underscore the transformative potential of localized NLP tools, with our ERM model offering practical applications for real-time sentiment analysis in Turkish-language contexts. By addressing critical areas, including marketing, public relations, and crisis management, these models facilitate improved decision-making through timely and accurate sentiment tracking. This highlights the significance of advancing research that accounts for regional and linguistic nuances.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
