Design and Application of Mapping Model for Emotion-Based Font Recommendation System

Abstract

Font usage is effective in accentuating meaning and establishing the overall tone of a message. Nevertheless, the process of selecting an appropriate font can be burdensome for users as it necessitates examining all available fonts. Furthermore, users with limited font usage experience might inadvertently choose an inappropriate font. To tackle this concern, we developed a system that recommends fonts by evaluating similarity between font keyword values and emotions extracted from content through deep learning emotion analysis. Considering the disparity in criteria utilized for classifying content emotions and font keywords, the necessity arose for a mapping model to evaluate the similarity between these two sets of criteria. Accordingly we designed our mapping model constructed based on the PAD model, a framework that represents emotions along three axes on a coordinate plane. We formulated two distinct methods to assess similarity: the first converts content and font characteristics into a single PAD value, subsequently discerning the distance; The second method analyzes the Pearson correlation coefficient between the criteria for emotional classification to determine the similarity. A comparative evaluation was conducted between these two methods. The results of the evaluation affirmed that the model reflecting the correlation coefficient yielded greater efficacy. As a result, we opted for this mapping model as the approach for calculating similarity between content and font.