Abstract
Many users use facemarks everyday in recent computer mediated communication environments such as e-mail, chatting, and Microblogs. Although facemarks are useful to express the emotion or communication intentions beyond natural language communication, many users feel difficult to choose the right one from lots of candidates according to the situation. We propose a method to recommend facemarks based on the estimation of emotions, communication, or motion types in texts written by users. Emotion, communication, or motion types are defined with Twitter corpus, and estimation system is implemented with k-NN. Five assessors evaluated the relevance of recommended facemarks for their intention, and found that 66.6% of facemarks for 91 tweets were recommended properly, which improved significantly over the recommendation only from emotion categories.
