###### Abstract
However, sentiment analysis of social media engagement allows museums to extract meaningful insights from followers' emotional responses to online content, which in turn can inform the creation of exhibits that emotionally connect with visitors. AI algorithms can automatically analyze the tone and emotional content of social media comments, identifying patterns that highlight visitor preferences. Meanwhile, IoT-connected devices within museum spaces can further integrate these data by linking them to in-person visitor behavior, creating a feedback loop between the digital and physical realms. By leveraging these technologies, museums can continuously adapt their exhibits to enhance both virtual and real-world experiences, leading to deeper emotional and cognitive engagement. There are variations in the popularity of different virtual formats, with some museums equally sought after across all social networks, while others, especially regional ones, thrive primarily on domestic social platforms. Notably, there is a growing trend among museums in establishing channels on YouTube, a phenomenon that was relatively rare in this institutional community not too long ago. It is increasingly evident that a truly modern museum in today's world is hardly conceivable without Social Media Marketing (SMM) [2]. This demonstrates the critical intersection between social media engagement and museum attendance, further explored in research like that of [PERSON] et al., which analyzes the influence of digital interactions on physical visits [1]. Museums serve as centers of culture, reflection, personal connections, and communication, often enhanced by human-computer interaction (HCI) systems. Research conducted at the Powell-Cotton Museum in the UK, for instance, analyzed visitors' emotional reactions to artifacts through structured interviews and thematic analysis. The results indicated that visitors strive to find meaningful and personal connections when asked to emotionally respond to artifacts [3]. This aligns with the broader trend of museums creating emotionally driven experiences, a phenomenon seen in projects like \"Sensitive Pictures\", co-created with the [PERSON] art museum. Here, visitors choose emotions, find corresponding paintings, and engage in emotional narratives, further connecting HCI with affective computing (AC) to analyze emotional responses via facial expression recognition [4]. Building on these principles, new recommendation systems for museums are now designed to personalize visitors' exhibition paths based on their emotional states. For example, at the Modern Art Museum \"Palazzo Buonaccorsi\" in Macerata, an interactive totem equipped with a touch screen and a Convolutional Neural Network for facial coding helps assess visitors' emotions, gender, and age, thereby curating personalized artwork suggestions. Extensive testing demonstrated that the system enhances the positivity of visitors' emotional experiences by creating an interactive and emotional connection with the artworks [5]. This aligns with the growing role of affective computing in museums, where emotion-driven technologies play a pivotal role in enhancing visitor engagement [6]. AI-driven emotion recognition systems can further enhance these experiences by analyzing visitors' real-time emotional states through facial recognition, voice analysis, or even wearable IoT devices that monitor physiological responses like heart rate or skin conductance. Sentiment analysis, applied to post-visit surveys or social media interactions, helps museums refine future exhibits to better match visitor expectations and emotional reactions. IoT devices installed in exhibits can provide real-time adjustments to lighting, audio, or display features based on detected emotional responses, making each museum visit a uniquely adaptive experience tailored to the visitor's emotional journey. The present work forms the basis for an extensive analysis of the emotional states of visitors via social media.