ISTC-CNR at EmotivITA: Towards Better Dimensional and multi-dimensional Analysis of VAD Emotions

Emotions are an integral part of human communication, and accurately detecting and interpreting them from textual data holds significant potential for numerous applications. The Valence-Arousal-Dominance (VAD) dimensional model provides a rich framework for capturing the nuanced emotional states conveyed through text. This paper presents an in-depth exploration of text-based emotion detection using VAD analysis and machine learning techniques. In this paper, we propose a novel machine-learning model specifically designed to detect VAD dimensions from textual data. Through empirical evaluation and comparison with existing methods, we analyze the results of our proposed model in accurately identifying emotions expressed in text. This paper describes the ISTC-CNR participation in the EmotivITA task at EVALITA 2023, showcasing our team's efforts and findings in the context of the competition.