iSelf: Towards cold-start emotion labeling using transfer learning with smartphones

Abstract

To meet the demand of more intelligent automation services on smartphone, more and more applications are developed based on users' emotion and personality. It has been a consensus that a relationship exists between personal emotions and usage pattern of smartphone. Most of existing work studies this relationship by learning manually labeled samples collected from smartphone users. The manual labeling process, however, is time-consuming, labor-intensive and money-consuming. To address this issue, we propose iSelf, a system which provides a general service of automatic detection for user's emotions in cold-start conditions with smartphone. Using transfer learning technology, iSelf achieves high accuracy given only a few labeled samples. We also develop a hybrid public/personal inference engine and validation system, so as to make iSelf maintain continuous update. Through extensive experiments, the inferring accuracy is tested about 75% and can be improved increasingly through validation and update.