Name
Abstract | ||
|---|---|---|
In this paper we provide the first evidence that daily happiness of individuals can be automatically recognized using an extensive set of indicators obtained from the mobile phone usage data (call log, sms and Bluetooth proximity data) and ``background noise'' indicators coming from the weather factor and personality traits. Our final machine learning model, based on the Random Forest classifier, obtains an accuracy score of 80.81% for a 3-class daily happiness recognition problem. Moreover, we identify and discuss the indicators, which have strong predictive power in the source and the feature spaces, discuss different approaches, machine learning models and provide an insight for future research. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/SocialCom.2013.118 | SocialCom |
Keywords | Field | DocType |
accuracy score,3-class daily happiness recognition,call log,final machine,happiness recognition,bluetooth proximity data,mobile phone data,background noise,different approach,random forest classifier,mobile phone usage data,daily happiness,learning artificial intelligence,mobile computing | Mobile computing,Computer science,Subjective well-being,Artificial intelligence,Happiness,Mobile phone,Affective computing,Ubiquitous computing,Random forest,Reality mining,Machine learning | Conference |
Citations | PageRank | References |
22 | 1.00 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrey Bogomolov | 1 | 75 | 5.60 |
| Bruno Lepri | 2 | 981 | 72.52 |
| Fabio Pianesi | 3 | 1109 | 88.84 |