Flood of techniques and drought of theories
Emotion Mining in Disasters
Bibliographic Data
| ID | 7158833 |
|---|---|
| Authors | Soheil Shapouri (0000-0001-9561-9751, Lehigh University, corresponding author), Saber Soleymani (University of Georgia), Saed Rezayi (University of Georgia) |
| Year | 2025 |
| Volume | 8 |
| Issue | 1 |
| Publication date | 2025-02-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Computational Social Science (JOURNAL) |
| Journal identifiers | ISSN: 2432-2725 • E-ISSN: 2432-2717 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s42001-024-00330-2 |
| OpenAlex | W4404868392 |
| Language | EN |
| References cited | 76 |
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
Archaeology · Flood myth · Geography · Sentiment Analysis and Opinion Mining · Spam and Phishing Detection · Terrorism, Counterterrorism, and Political Violence · Geology
Appraisal Theories of Emotion
Cross-Cultural Differences in Risk Perceptions of Disasters
To Explain or to Predict?
Vader
Beyond Western, Educated, Industrial, Rich, and Democratic (Weird) Psychology
Differences Between Tight and Loose Cultures
A Neural-Based Approach for Detecting the Situational Information From Twitter During Disaster
Domain-specific sentiment analysis for tweets during hurricanes (DSSA-H)
Machine Learning-Based Data Mining Method for Sentiment Analysis of the Sewol Ferry Disaster's Effect on Social Stress
A circumplex model of affect
An argument for basic emotions
Emotions and beliefs after a disaster
Disaster Misinformation and Its Corrections on Social Media
Mining Twitter Data for Improved Understanding of Disaster Resilience
| Highly cited | No |
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