# Implementing and Evaluating a Font Recommendation System Through Emotion-Based Content-Font Mapping
###### Abstract  
Rapid digital content growth demands pivotal font selection for design and communication. Our study focuses on a font recommendation system that aligns fonts with content emotions. To achieve this, we define font-emotions and quantify them. Additionally, we leverage deep learning techniques for content analysis. Understanding common emotional perceptions, we aimed to align fonts with content emotions. After evaluating diverse mapping methods, we determined a correlation analysis-based model to be most effective. Implementing this model, we verified its utility through usability evaluations. Our proposed system not only assists users with limited design knowledge in receiving contextually fitting font suggestions but also extends its application across various digital content realms. font recommendation system; content emotion analysis; emotion calculation models; usability evaluation; emotion-based font recommendation +
Footnote †: journal: Medical  
directly select the emotions perceived from the fonts. In this paper, we aim to recommend fonts based on the impression data collected from previous research on fonts. Ultimately, we successfully implemented a font recommendation system that suggests fonts aligned with the emotional context of the content. Given the distinct classification criteria of content emotion and font keywords, we needed a mapping model that could enable comparisons between different emotional classification standards. To achieve this, we designed two mapping methods: one that converts content and font emotions into PAD (Pleasure, Arousal, Dominance) values for comparison, and the other that relies on correlation analysis between content emotion classification standards and font keywords. We compared the effectiveness of these two mapping methods, presenting font recommendation lists for content using each calculation model. The results allowed us to evaluate and choose the model deemed most suitable for the task, confirming that the correlation analysis-based mapping method was more effective. This paper makes the following contributions:  
* Design and Implementation of a Font Recommendation System: The research focused on designing and implementing a font recommendation system based on emotional context. This system enables designers to quickly and accurately receive font suggestions that align with the emotional tone of the input content. * Development of Mapping Models for Font and Content Emotions: The study introduced two mapping models to bridge the gap between different emotional classification standards for content and font keywords. These models allow for comparisons between various emotional classification standards, laying the foundation for tailored recommendations. * Implementation of a Font Recommendation Interface: Using a correlation analysis-based mapping method, the research implemented a font recommendation interface. This interface actualizes font suggestions based on content emotion to enhance the user experience. Usability evaluations were conducted to validate the effectiveness of the system. Our study proposes a font recommendation system based on content emotion, enabling users with limited design experience to quickly and accurately receive font suggestions tailored to the context. The approach we have presented can find applications in various digital content fields, including video production, electronic publishing, and social networks. Figure 1 represents the entire process of our font recommendation system. The structure of this paper is as follows: In Section 2, various studies related to font recommendation systems and emotion analysis are discussed. Section 3 presents the research findings regarding emotional keywords associated with fonts, including the selection of keywords to express font impressions and the calculation of keyword attribute values for each font. This research was conducted in-depth in previous studies.