Measuring the Polarity of Conversations between Chatbots and Humans: A Use Case in the Banking Sector

Guillaume Noé-Bienvenu (1) , Damien Nouvel (2) , Djamel Mostefa (1)

1 Orange Bank
2 ERTIM - Équipe de Recherche en Textes, Informatique, Multilinguisme

This paper describes a study on opinion analysis applied to both human to chatbot conversations, but also to human to human conversations using data coming from the banking sector. A polarity classifier SVM model applied to conversations provides insights and visualisations of the satisfaction of users at a given time and its evolution. We conducted a study on the evolution of the opinion on the conversations started with the chatbot and then transferred to a human agent. This work illustrates how opinion analysis techniques can be applied to improve the user experience of the customers but also detect topics that generate frustrations with a chatbot or with human experts.

Domaines: Informatique [cs]; Traitement du texte et du document

Guillaume Noé-Bienvenu, Damien Nouvel, Djamel Mostefa. Measuring the Polarity of Conversations between Chatbots and Humans: A Use Case in the Banking Sector. 15th Federated Conference on Computer Science and Information Systems (FedCSIS 2020), Sep 2020, Sofia, Bulgaria. ⟨10.15439/2020f63⟩. ⟨hal-03613414⟩