title: Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study.
abstract: BACKGROUND
  Prompt recognition and intervention of negative emotions is crucial for patients with depression. Mobile phones and mobile apps are suitable technologies that can be used to recognize negative emotions and intervene if necessary.
  
  
  OBJECTIVE
  Mobile phone usage patterns can be associated with concurrent emotional states. The objective of this study is to adapt machine-learning methods to analyze such patterns for the prediction of negative emotion.
  
  
  METHODS
  We developed an Android-based app to capture emotional states and mobile phone usage patterns, which included call logs (and use of apps). Visual analog scales (VASs) were used to report negative emotions in dimensions of depression, anxiety, and stress. In the system-training phase, participants were requested to tag their emotions for 14 consecutive days. Five feature-selection methods were used to determine individual usage patterns and four machine-learning methods were tested. Finally, rank product scoring was used to select the best combination to construct the prediction model. In the system evaluation phase, participants were then requested to verify the predicted negative emotions for at least 5 days.
  
  
  RESULTS
  Out of 40 enrolled healthy participants, we analyzed data from 28 participants, including 30% (9/28) women with a mean (SD) age of 29.2 (5.1) years with sufficient emotion tags. The combination of time slots of 2 hours, greedy forward selection, and Naïve Bayes method was chosen for the prediction model. We further validated the personalized models in 18 participants who performed at least 5 days of model evaluation. Overall, the predictive accuracy for negative emotions was 86.17%.
  
  
  CONCLUSIONS
  We developed a system capable of predicting negative emotions based on mobile phone usage patterns. This system has potential for ecological momentary intervention (EMI) for depressive disorders by automatically recognizing negative emotions and providing people with preventive treatments before it escalates to clinical depression.
authors: Department of General Psychiatry, Taipei City Psychiatric Center, Taipei City Hospital, Taipei, Taiwan. galenhung@tpech.gov.tw.
authors: Galen Chin-Lun
authors: http://orcid.org/0000-0001-6333-7880
authors: GC
authors: Hung
authors: Pei-Ching
authors: http://orcid.org/0000-0002-2553-0695
authors: PC
authors: Yang
authors: Chia-Chi
authors: http://orcid.org/0000-0003-0245-2839
authors: CC
authors: Chang
authors: Jung-Hsien
authors: http://orcid.org/0000-0002-4657-6705
authors: JH
authors: Chiang
authors: Ying-Yeh
authors: http://orcid.org/0000-0001-5543-5704
authors: YY
authors: Chen
keywords: affective computing; depression; emotion; machine learning; mobile phone usage