Abstract :

The Tourism Industry Frequently Encounters Emergency Situations Such As Natural Disasters, Health Outbreaks, Transportation Failures, And Security Threats That Can Significantly Affect The Emotional State And Safety Of Travelers. During Such Crises, Tourists Often Experience Emotions Such As Fear, Anxiety, Confusion, And Panic, Which Can Influence Their Behavior And Decision-making. Traditional Emergency Management Systems Primarily Focus On Physical Safety Measures And Operational Protocols, Often Neglecting The Psychological And Emotional Needs Of Tourists. To Address This Limitation, This Study Proposes An AI-driven Emotion Recognition And Crisis Management Framework Designed To Detect And Manage Tourist Emotions In Real Time During Emergency Situations. The Proposed System Utilizes Artificial Intelligence Techniques Including Machine Learning, Deep Learning, And Natural Language Processing To Analyze Facial Expressions, Voice Signals, Behavioral Patterns, And Textual Feedback. By Identifying Emotional States Such As Fear, Stress, Or Confusion, The System Enables Tourism Authorities And Emergency Response Teams To Deliver Personalized Guidance, Targeted Assistance, And Adaptive Communication Strategies. The Integration Of Multimodal Emotion Detection With Intelligent Crisis Management Improves Situational Awareness, Reduces Panic, And Enhances The Effectiveness Of Emergency Response Mechanisms. Experimental Evaluations Demonstrate That The Proposed Framework Can Significantly Improve Crisis Communication, Optimize Resource Allocation, And Support Safer And More Resilient Tourism Environments. Keywords: Artificial Intelligence, Emotion Recognition, Crisis Management, Tourism Industry, Emergency Situations, Multimodal Emotion Detection, Tourist Safety

Published:

07-3-2026

Issue:

Vol. 26 No. 3 (2026)

Page Nos:

62-68

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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