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

It has been a consensus that a certain relationship exists between personal emotions and usage pattern of the smartphone. Based on users’ emotions and personalities, more and more applications are developed to provide intelligent automation services on the smartphone, such as music recommendations or stranger introductions on social networking sites. Most existing work studies this relationship by learning large amounts of samples, which are manually labeled and collected from smartphone users. The manual labeling process, however, is very time-consuming and labor-intensive. To address this issue, we propose iSelf, a system that provides a general service of automatic detection of a user’s emotions in cold-start conditions with a smartphone. With the technology of transfer learning, iSelf achieves high accuracy given only a few labeled samples. We also embed a hybrid public/personal inference engine and validation system into iSelf, to make it maintain updates continuously. Through extensive experiments in real traces, the inferring accuracy is tested above 74% and can be improved increasingly through validation and updates. The application program interface has been open online for other developers.