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.
- E. Marg, 1995, "Descartes'error: Emotion, reason, and the human brain," Optometry & Vision Science, 72(11), pp. 847--848. Google ScholarCross Ref
- T. Saari, K. Kallinen, M. Salminen, N. Ravaja, and K. Yanev, "A mobile system and application for facilitating emotional awareness in knowledge work teams," In HICSS, Jan 7--10, 2008.Google Scholar
- A. Gluhak, M. Presser, L. Zhu, S. Esfandiyari, and S. Kupschick, "Towards mood based mobile services and applications," In Smart Sensing and Context, 2007, pp. 159--174.Google Scholar
- K. K. Rachuri, et al., "EmotionSense: a mobile phones based adaptive platform for experimental social psychology research," Proceedings of the12th ACM international conference on Ubiquitous computing. ACM, 2010, pp. 281--290. Google ScholarDigital Library
- H. Lu, et al., "Stresssense: Detecting stress in unconstrained acoustic environments using smartphones," Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM, 2012, pp. 351--360. Google ScholarDigital Library
- M. Roshanaei, S. Mishra, "Studying the attributes of users in Twitter considering their emotional states," Social Network Analysis and Mining, Springer, 2015, pp. 5--34.Google Scholar
- M. Roshanaei, S. Mishra, "Having Fun?: Activity-based Mood Prediction in Social Media. Accepted in Social Network Analysis Lecture Notes Series," 2017, pp. 1--18.Google Scholar
- N. Yang and A. Samuel. "Context-rich detection of users emotions using a smartphone,".Google Scholar
- R. Wang, et.al., "StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones," In Proceedings of the 2014. ACM International Joint Conference on Pervasive and Ubiquitous Computing. (UbiComp '14). ACM, New York, NY, USA, 2014, pp. 3--14. Google ScholarDigital Library
- J. E. Bardram, M. Frost, K. Szántó, and G. Marcu, "The MONARCA self-assessment system: a persuasive personal monitoring system for bipolar patients," In Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium (IHI '12). ACM, New York, NY, USA, 2012, pp.21--30. Google ScholarDigital Library
- H. Lee, Y. S. Choi, S. Lee, and IP Park. "Towards unobtrusive emotion recognition for affective social communication," In Consumer Communications and Networking Conference (CCNC), 2012, pp. 260--264. Google ScholarCross Ref
- R. LiKamWa, Y. Liu, N. D. Lane, and L. Zhong, "Moodscope: building a mood sensor from smartphone usage patterns," In Proceeding of the 11th annual international conference on Mobile systems, applications, and services, 2013, pp. 389--402.Google Scholar
- Y. Huang, Y. Tang, Y. Tang. "Emotion Map: A Location-based Mobile Social System for Improving Emotion Awareness and Regulation," In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work \& Social Computing, 2015, pp. 130--142. Google ScholarDigital Library
- M. D. Choudhury, M. Gamon, S. Counts, and E. Horvitz, "Predicting depression via social media," In ICWSM, 2013.Google Scholar
- A. H. Fischer, and A. SR. Manstead. "The relation between gender and emotions in different cultures," Gender and emotion: Social psychological perspectives, 2004, 4 (1), pp. 87--94Google Scholar
- EmotionSensing: Predicting Mobile User Emotion
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