## 5 A New Model for Representing Emotional and Affective States in the Museum Context  
Using the scenario and context presented in the previous sections, here, we present our work to establish a methodology for identifying and quantifying the emotional state of museum visitors from a computational perspective. This work does not claim to provide a definitive approach to assessing emotional or affective states, but it specifically focuses on developing a computational model for evaluating emotions and affective states within the realm of ICT (Information and Communication Technology) in museum contexts.  
Given the variety of technologies that can now be used to enhance museum interaction in virtual settings, this section aims to provide a solid methodological foundation that can be applied across the board, regardless of the technology used. Moreover, as we will see in the following sections, this methodology was employed through questionnaires distributed via social media to a sample of 1000 learners. Although 1000 tests are a significant number, especially for a scientific article, in this work, we considered it the minimum threshold for experimentation. In the context of the Future Internet, the goal is for future studies by the present authors or other colleagues to achieve even higher numbers, with the potential to reach millions of visitors virtually guided in the enjoyment of museum services via social media.  
Drawing from extensive research and the analysis of major paradigms and models for managing emotions and affections within Intelligent Tutoring Systems (ITSs), we refer to the emotions highlighted by [PERSON] et al. in [44], emphasizing their relevance in the context of learning. [PERSON]'s work builds upon [PERSON]'s categorization of emotions and identifies four key classes of basic emotions. We represent their classifications along two axes (see Figures 1 and 2).  
The assessment of affective and emotional states is designed to serve as inputs, allowing users to assign values to a state (character's emotion) on two levels:  
1. State Identification: Provides Boolean feedback (yes/no).
2. Measurement/Quantification of the State: Uses a scale from \(-1\) to \(1\).  
To assess emotions as Boolean types, a questionnaire with twelve questions is employed, i.e., with three questions for each axis. This questionnaire leads to the assignment of a score where \(-1\) corresponds to an extreme emotion, \(1\) corresponds to the opposite extreme, and \(0\) represents indifference towards that emotion.  
For quantitative emotional assessment, specific emotions are identified and mapped on a scale of \(1\) to \(10\) through ten targeted questions, each tailored to a particular emotional class.  
Our approach is delineated in the following sections. Both emotivity and affectivity states are analyzed as distinct four-dimensional spaces. The model comprises the following steps:  
* **Step Level 1: Stimulus-Response:** This step indicates whether the visitor responds positively, negatively, or indifferently to emotional/affective stimuli provided via the questionnaire.
* **Step Level 2: Output Response--Quantity:** Parameters are quantified on a scale ranging from \(1\) to \(10\) through ten specific questions.
* **Step Level 3: Estimation of Dominance:** Tuple parameters are generalized concerning the output of the second step and the weighting given to each of the visitor's parameters.
* **Step Level 4.1: Evaluation of Emotivity/Affectivity:** A set of parameters is quantified, including absolute emotion, emotional arrangement, and others.
* **Step Level 4.2: Characterization of Emotivity/Affectivity:** Building upon values obtained in the previous step, we determine the extent to which a visitor experiences one emotion more strongly than another in the context of their museum experience.