###### Abstract
This is achieved through the definition of a specific methodology and a new model of emotional computing based on the acquisition of data in the physical field with a questionnaire administered to 1000 learners. First, here is a short overview of the emotional computing context of interest in the present paper. Sentiment analysis and emotion recognition technologies contribute to this growing field by harnessing machine learning models for real-time emotional analysis. While some approaches rely on social media data and text analysis, others, like EEG-based emotion recognition, provide more accurate physiological measurements. For instance, the study at Tianyi Pavilion Museum used EEG signals combined with the PAD emotional model to measure visitors' emotional tendencies in real-time, demonstrating the potential of physiological signals to offer deeper insights into emotional engagement [7; 8]. This model could complement the sentiment analysis we propose, which involves analyzing emotional responses gathered through online questionnaires and social media interactions. The integration of [PERSON]'s Emotion and Adaptation (EMA) model offers a strong theoretical framework for understanding how emotions impact decision-making and behavior, further informing the design of these digital experiences [9; 10]. Our work builds on the previous foundations by developing a new model of emotional computing that combines social media data, sentiment analysis, and cognitive-emotional theory to measure the emotional states of visitors. Through a comprehensive methodology that includes questionnaires and sentiment analysis across social media platforms, we aim to bridge the gap between digital and physical museum experiences, expanding the potential for affective computing in modern museology. Ultimately, as we continue to explore the intersection between social media, affective computing, and visitor engagement, it becomes clear that emotional experiences play a crucial role in fostering both online and physical interactions with museum contents [11; 12]. Integrating AI, IoT, and sentiment analysis into museum environments aligns with the Future Internet's overarching goals by ensuring that exhibitions' content and context can dynamically adapt to visitors' emotional and cognitive states. These technologies enable museums to transition from static information repositories to interactive, emotionally attuned spaces that respond to the needs and preferences of each visitor in real-time. As AI models become more sophisticated and IoT technologies evolve, we anticipate that museums will offer even more personalized, immersive, and emotionally impactful experiences. By integrating multimodal emotion recognition technologies, museums can further personalize and enhance visitor experiences, offering a more emotionally resonant and enriching environment that transcends traditional museum boundaries [13]. Our research contributes to the evolving field of digital museology and affective computing by proposing a new model that integrates social media data, sentiment analysis, and cognitive-emotional theories. By bridging these areas, we aim to enhance the understanding of emotional engagement in museum experiences, both in the digital realm and within physical spaces [14; 15]. Then, the objective of this work is to develop a computational model designed to enhance the emotional aspect of learning experiences within museum environments. The focus is on representing and managing affective and emotional feedback to understand how emotions significantly impact the learning process in a museum context. The proposed model aims to identify and quantify emotions during a visitor's engagement with museum exhibits. The key goals include exploring methods and techniques for assessing and recognizing emotional responses in museum visitors, as well as feedback management strategies based on the detection of visitors' emotional states.