EmotionSensing: Predicting Mobile User Emotion

Abstract

User emotions are important contextual features in building context-aware pervasive applications. In this paper, we explore the question of whether it is possible to predict user emotions from their smartphone activities. To get the ground truth data, we have built an Android app that collects user emotions along with a number of features including their current location, activity they are engaged in, and smartphones apps they are currently running. We deployed this app for over a period of three months and collected a large amount of useful user data. We describe the details of this data in terms of statistics and user behaviors, provide a detailed analysis in terms of correlations between user emotions and other features, and finally build classifiers to predict user emotions. Performance of these classifiers is quite promising with high accuracy. We describe the details of these classifiers along with the results.