Sentiment-Aware Dialogue Flow Discovery for Interpreting Communication Trends
Patrícia Ferreira, Isabel Carvalho, Ana Flávia Elói da Silva Alves, Catarina Silva, Hugo Gonçalo Oliveira · 2024
Customer-support services increasingly rely on automation, whether full or with human intervention.Despite optimising resources, this may result in mechanical protocols and lack of human interaction, thus reducing customer loyalty.Our goal is to enhance interpretability and provide guidance in communication through novel tools for easier analysis of message trends and sentiment variations.Monitoring these contributes to more informed decision-making, enabling proactive mitigation of potential issues, such as protocol deviations or customer dissatisfaction.We propose a generic approach for dialogue flow discovery that leverages clustering techniques to identify dialogue states, represented by related utterances.State transitions are further analyzed to detect prevailing sentiments.Hence, we discover sentimentaware dialogue flows that offer an interpretability layer to artificial agents, even those based on black-boxes, ultimately increasing trustworthiness.Experimental results demonstrate the effectiveness of our approach across different dialogue datasets, covering both human-human and human-machine exchanges, applicable in task-oriented contexts but also to social media, highlighting its potential impact across various customer-support settings.