A CNN-Based Tool to Index Emotion on Anime Character Stickers

Anime character stickers consist of a detailed illustration of characters that represents emotions. Generally, message apps let users save received stickers, but in some cases, the task of manual searching for a specific sticker may be frustrating when a large amount of them are stored. In this work, we propose a CNN-based tool for emotion indexing of message stickers. We built a dataset with 12.668 labeled stickers with 3 classes (Sad, Happy and Angry). In experiments, our model achieves 84.01% of global f1-score. Additionally, we describe our proposed tool for emotion indexing of message stickers that has three main functionalities: (1) sticker recommendation function that classifies stickers and recommends the class desired; (2) filter function that takes out the stickers it considers mislabeled; (3) ranking function that orders the labeled stickers.