Leveraging NLP to Analyze Emotions in Customer-Agent Interactions: Impacts on Satisfaction and Recommendation Intentions
Citations
Altmetric:
Publication Type
Conference Proceeding
Editor
Supervisor
Publication Year
2025
Journal
Book
Publication Volume
Publication Issue
Publication Begin page
Publication End page
Publication Number of pages
Collections
Abstract
We investigate the impact of customer and agent emotions, as well as emotional matching, on satisfaction and recommendation intentions in a utilitarian service context. Employing transformer-based NLP algorithms, we analyze observed data from 25,008 call center conversations and compare our findings with prior survey-based research. Our analysis reveals that positive customer sentiment more strongly influences satisfaction and recommendation than negative sentiment. Negative emotions, while less impactful than positive ones, have a relatively greater effect on recommendation than on satisfaction. Agent emotions have a smaller impact on both outcomes compared to customer emotions. Emotional matching is generally beneficial, except when dealing with high-arousal negative emotions like anger. Our conceptual framework is grounded in theories of delight, formality, source credibility, emotional arousal, and loss aversion.