Using State-of-the-art Emotion Detection Models in a Crisis Communication Context


  • Luna De Bruyne Ghent University
  • Toni G.L.A van der Meer University of Amsterdam
  • Orphée De Clercq Ghent University
  • Véronique Hoste Ghent University



sentiment analysis, emotion detection, automated content analysis, evaluation, validity, crisis communication


Times of crisis are usually associated with highly emotional experiences, which often result in emotionally charged communication. This is especially the case on social media. Identifying the emotional climate on social media is imperative in the context of crisis communication, e.g., in view of shaping crisis response strategies. However, the sheer volume of social media data often makes manual oversight impossible. In this paper, we therefore investigate how automatic methods for emotion detection can aid research on crisis communication and social media. Concretely, we investigate two Dutch emotion detection models (a transformer model and a classical machine learning model based on dictionaries) and apply them to Dutch tweets about four different crisis cases. First, we perform a validation study to assess the performance of these models in the domain of crisis-related tweets. Secondly, we propose a framework for monitoring the emotional climate on social media, and assess whether emotion detection models can be used to address the steps in the framework.




How to Cite

De Bruyne, L., van der Meer, T. G., De Clercq, O., & Hoste, V. (2024). Using State-of-the-art Emotion Detection Models in a Crisis Communication Context. Computational Communication Research, 6(1).