Emotion mining has become a crucial tool for understanding human emotions during disasters, leveraging the extensive data generated on social media platforms. This paper aims to summarize existing research on emotion mining within disaster contexts, highlighting both significant discoveries and persistent issues. On the one hand, emotion mining techniques have achieved acceptable accuracy enabling applications such as rapid damage assessment and mental health surveillance. On the other hand, with many studies adopting data-driven approaches, several methodological issues remain. These include arbitrary emotion classification, ignoring biases inherent in data collection from social media, such as the overrepresentation of individuals from higher socioeconomic status on Twitter, and the lack of application of theoretical frameworks like cross-cultural comparisons. These problems can be summarized as a notable lack of theory-driven research and ignoring insights from social and behavioral sciences. This paper underscores the need for interdisciplinary collaboration between computer scientists and social scientists to develop more robust and theoretically grounded approaches in emotion mining. By addressing these gaps, we aim to enhance the effectiveness and reliability of emotion mining methodologies, ultimately contributing to improved disaster preparedness, response, and recovery. Keywords: emotion mining, sentiment analysis, natural disasters, psychology, technological disasters

翻译：情感挖掘已成为理解灾害期间人类情感的关键工具，其利用社交媒体平台产生的大量数据。本文旨在总结灾害情境下情感挖掘的现有研究，突出重要发现与持续存在的问题。一方面，情感挖掘技术已达到可接受的准确度，支持快速损害评估和心理健康监测等应用。另一方面，由于许多研究采用数据驱动方法，若干方法论问题依然存在。这些问题包括任意的情感分类、忽视社交媒体数据收集的固有偏差（如Twitter上较高社会经济地位个体的过度代表），以及缺乏跨文化比较等理论框架的应用。这些问题可概括为理论驱动研究的显著缺失和对社会行为科学见解的忽视。本文强调计算机科学家与社会科学家之间跨学科合作的必要性，以开发更稳健且具有理论依据的情感挖掘方法。通过弥补这些不足，我们旨在提升情感挖掘方法的有效性和可靠性，最终为改善防灾备灾、应急响应和灾后恢复做出贡献。关键词：情感挖掘，情感分析，自然灾害，心理学，技术灾害