A generative AI model for modelling and creating an emotion-aware database: Design, and analytics

In this study, we illustrate an ongoing work regarding the building of an ad hoc textual dataset for dialogues between a user and an agent of a customer care center. The user is required to express a given emotion and to use a specific level of the Common European Framework of Reference for Languages (CEFR). Once the criteria had been defined, we asked ChatGPT 3.5 to generate dialogues. We analyzed them to ensure an acceptable level of quality for the dataset. Furthermore, we measured the language complexity used both by the user and the agent through different readability measures. Moreover, we extracted from each turn of each dialogue the attitude that ChatGPT exhibited for each dialogue turn; the interaction patterns were then analyzed with algorithm Sequential Pattern Mining (SPM) to extract typical interaction mechanisms occurring in specific emotional contexts. The generated dataset of dialogues, enriched with the readability measures and the extracted patterns, can be exploited as a system that facilitates the interaction between humans and artificial agents in specific contexts and it can constitute the basis of a reinforcement learning system for improving the effectiveness of the dialogues. • We focus on a model based on generative AI for modelling and creating a database that supports emotion recognition and handling. • We define a general architecture for our generation framework. • We provide a comprehensive experimental evaluation of the proposed model.