# Implementing and Evaluating a Font Recommendation System Through Emotion-Based Content-Font Mapping
## 6 Conclusions  
In conclusion, this research aimed to implement a system that recommends fonts based on the emotions of the content. To achieve this, the emotions associated with fonts were analyzed using keywords. We selected 19 keywords that are applicable to Korean fonts and easily understandable by users, based on keywords used in previous studies on English fonts. We conducted experiments to examine the relationship between keywords and fonts, selecting 26 fonts representative of various font classifications. Figure 8: Group-specific results for satisfaction scores. Figure 9: The existing font recommendation system was described in previous research. (Reprinted/adapted with permission from Ref. [13]. Copyright 2018, copyright [PERSON]). To recommend fonts based on content emotions, we designed new calculation models to allow for a consistent comparison between content emotions and font emotions. While we initially attempted to apply the PAD emotional model, convergence issues arose during the process of converting keyword attributes to PAD values. As a result, we designed two new calculation models: one with a new formula for PAD value conversion and another that utilized an analysis of the correlation between emotional classification criteria. The evaluation results favored the model that applied the correlation-based approach. Therefore, we decided to use this mapping method for font recommendations. We implemented the recommendation interface using this model and conducted usability evaluations. The findings confirmed the appropriateness of the fonts recommended by the system and the efficiency of our implementation compared to the existing system. In summary, this research successfully analyzed font emotions through keyword analysis and recommended fonts suitable for content with varying classification criteria. Furthermore, we expanded this recommendation system to platforms such as blogs, which encompass a diverse array of content. As a result, users can receive font recommendations based on the mood and emotions of their creative works during their creative activities, allowing for convenient and suitable font usage. Ultimately, this enables users to easily attain the positive effects achievable through font utilization. The flexibility of our matching model allows for the recommendation of fonts for a wide range of diversified content. By enhancing font selection, even users with limited design expertise can improve the quality of their creations while reducing the cost associated with font selection. We believe that the concept introduced in this study can be extended to various types of content beyond facial expressions and sentences. In the future, we plan to conduct an in-depth system evaluation targeting font designers and other experts, separate from the usability assessment. Through this, we aim to measure the accuracy of our font recommendation algorithm and system, assess user satisfaction, and identify areas for further enhancement. Subsequently, we will perform actual usability testing by providing font recommendations to users free of charge, aiming to refine the system for improved temporal efficiency in font selection. Conceptualization, S.-B.L. and Y.-S.J. ; methodology, S.-B.L., B.A. and S.-B.L. ; validation, S.-B.L. and Y.-S.J. ; formal analysis, B.A. and J.H.P. ; writing\(-\)original draft preparation, Y.S. ; writing\(-\)review and editing, Y.S. ; project administration, S.-B.L. All authors have read and agreed to the published version of the manuscript. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2021R1I1A4A01059550). This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (grant number: RS-2022-00165818). Not applicable. The data presented in this study are available on request from the corresponding author.