## 3 Enhancing Visitor Experiences Through Text Analysis and Sentiment Assessment  
In this section, we delve into the methods and techniques employed within the museum context to enhance visitor experiences through text analysis and sentiment assessment. The study of how visitors express their emotions through written text or how specific textual content evokes different emotional responses is vital in the museum domain. From the historical point of view, this approach resonates with the well-known work by [PERSON], who pioneered research in this area. [PERSON] used multidimensional scaling (MDS) to create displays of emotional words, classifying them based on the similarities between words. A diverse set of words was provided and rated by individuals from various cultural backgrounds. These words can be envisioned as points in a multidimensional space, with the distance between pairs of words signifying their similarity. The dimensions that emerged from this work included \"evaluation\", \"potency\", and \"activity\". Evaluation quantifies the pleasantness or unpleasantness of a word, resembling hedonic valence. Potency indicates the intensity level associated with a word, while activity relates to whether a word is active or passive. These dimensions closely align with the circumplex model of emotion, which includes valence and arousal, recognized as fundamental in describing emotional states [25; 26]. More recently, researchers like [PERSON] and [PERSON] [27] have used French and English dictionaries to construct \"conceptual maps of value\", akin to [PERSON]'s work, and found similar underlying dimensions. Another research line explores lexical analysis of text to identify words predictive of visitors' emotional states [28; 29; 30; 31; 32; 33]. This approach often builds upon linguistic research. For instance, the Linguistic Inquiry and Word Count (LIWC) is a validated tool used for analyzing text by categorizing it based on a dictionary. Techniques based on LIWC aim to identify specific items revealing the emotional content in the text. For example, first-person singular pronouns (e.g., \"I\" and \"me\") have been linked to negative feelings [30; 34; 35]. Text analysis holds particular importance in the museum context due to the text-based interfaces frequently employed in interactive exhibits. Sentiment analysis involves classifying text based on its overall sentiment. Sentiment analysis, also known as Opinion Mining, plays a significant role in the museum domain by extracting and interpreting opinions expressed in documents or text. Typically, sentiment analysis categorizes text into \"positive\" and \"negative\" sentiment categories. The sentiment polarity classification aims to determine whether an opinion expressed in text is \"positive\" or \"negative\" or where it falls on the continuum between these two extremes [33]. In sentiment analysis for museums, the classification can take various forms, including binary categorization (e.g., classifying text as \"positive\" or \"negative\"), regression (e.g., assigning a numerical value between 0, representing \"extremely negative\", and 10, representing \"extremely positive\"), or ranking (e.g., determining which text exhibits a more positive sentiment on a given topic). Sentiment polarity classification is typically based on two opposing classes, such as \"positive\" and \"negative\". A fascinating area of research in sentiment analysis in the museum context focuses on affect analysis. Affect analysis seeks to identify and extract emotionally charged text and subsequently classify the emotional sentiment expressed. This involves the identification of specific emotions appearing in the text as a form of opinion [36; 37]. Many studies in this area draw inspiration from the six universal emotions described by [PERSON]: anger, disgust, fear, happiness, sadness, and surprise [18].