Name
Title | ||
|---|---|---|
Moodee: An Intelligent Mobile Companion for Sensing Your Stress from Your Social Media Postings. |
Abstract | ||
|---|---|---|
In this demo, we build a practical mobile application, Moodee, to help detect and release users' psychological stress by leveraging users' social media data in online social networks, and provide an interactive user interface to present users' and friends' psychological stress states in an visualized and intuitional way. Given users' online social media data as input, Moodee intelligently and automatically detects users' stress states. Moreover, Moodee would recommend users with different links to help release their stress. The main technology of this demo is a novel hybrid model - a factor graph model combined with Deep Neural Network, which can leverage social media content and social interaction information for stress detection. We think that Moodee can be helpful to people's mental health, which is a vital problem in modern world. |
Year | Venue | Field |
|---|---|---|
2016 | THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | Social relation,Factor graph,World Wide Web,Social network,Social media,Computer science,Mental health,Artificial neural network,User interface,Psychological stress |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
1 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huijie Lin | 1 | 53 | 4.47 |
| Jia Jia | 2 | 451 | 55.08 |
| Jie Huang | 3 | 292 | 20.71 |
| Enze Zhou | 4 | 0 | 1.01 |
| Jingtian Fu | 5 | 8 | 1.48 |
| Yejun Liu | 6 | 90 | 18.35 |
| Huanbo Luan | 7 | 861 | 45.39 |