# Implementing and Evaluating a Font Recommendation System Through Emotion-Based Content-Font Mapping
## 2 Related Works  
The exploration of mapping fonts to context and emotions has been a recurring area of investigation, underscoring the significance of font selection in various aspects of communication and user perception. In one study [7], researchers delved into the connections between the visual attributes of fonts and the linguistic context of the text. This research is expected to have a substantial impact on our understanding of how text and fonts interact. Additionally, current research [8] is concentrated on unraveling the emotional nuances conveyed by Chinese character fonts. This research promises to bring new insights into how fonts can evoke emotions, further enhancing our comprehension of the link between font choice and emotional expression. As highlighted by these studies, the selection of an appropriate font holds great significance for communication and the way users perceive information. Previous research, such as the exploration of font importance in email communication [9] and the emphasis on the scientific principles of font selection in academic writing [10], has demonstrated that specific fonts can influence the overall aesthetic appeal of text and the message's impact. Moreover, these studies have established methods for choosing fonts that improve text quality and readability. Furthermore, research focusing on the psychological aspects of fonts and readability [11] has revealed that design elements like font style, thickness, spacing, and color can shape a reader's aesthetic sensibility and comprehension. Certain fonts can either emphasize or tone down the emotional undertones of the text, contributing to a more comprehensive understanding of the message. In this context, the importance of font selection in various digital media, such as blogs, cannot be overstated. It is a critical factor in effective communication and message reception in today's digital age. In line with the importance of font selection, recent research has been actively utilizing state-of-the-art deep learning algorithms to recommend fonts [4; 5]. These studies underscore the significance of context-specific font selection and employ deep learning and machine learning algorithms to assist font selectors in making more appropriate font choices. To recommend fonts based on emotions, previous studies have measured and suggested the emotional attributes of fonts [12; 13]. These studies conducted emotion analysis on both English and Korean fonts. Subsequently, after exploring fundamental attributes in the classification system of Korean fonts, emotional attribute values were acquired, leading to the implementation of an emotion-based recommendation system. Inspired by this research, our goal was to recommend fonts based on emotions perceived within the content. Well-known commercial font recommendation systems include Adobe Fonts (Adobe Creative Cloud) [14], Google Fonts [15], and Canva (Font Space) [16]. These platforms facilitate font searching and assist in making appropriate font selections through features like simple filters. Adobe Fonts, integrated with some of Adobe's design tools, offers a vast array of fonts. Designers utilize Adobe Fonts to find fonts suitable for their projects, and the service provides recommendation features to assist users in selecting fitting fonts. Google Fonts provides a variety of free web fonts, allowing users to review detailed information about each font and search for fonts that suit their projects. However, users need to manually search using filters, and the service focuses more on selecting fonts based on their appearance than the mood of the context in which they will be used. Canva, a tool for video creation, social media posters, web design, and more, includes built-in font searching and recommendation features. Canva enables users to search for fonts based on style, using keywords like cool, cute, and fancy. However, some features are paid, and users need to manually search for fonts that match the desired mood.