  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
