Paper Info
Title
A hybrid system for online detection of emotional distress
Abstract
Nowadays, people are familiar with online communication and tend to express their deeper feelings on the Web. In the light of this situation, we present a hybrid system based on affect analysis for mining emotional distress tendencies from publicly available blogs to identify needy people in order to provide timely intervention and promote better public health. We describe the system architecture with a hand-crafted model at a fine level of detail. The model, which incorporates human judgment, enables the adjustment of prediction in machine learning on blog contents. The system blending supervised and unsupervised approaches will be examined and evaluated in lab experiments and practice.
Year
DOI
Venue
2012
10.1007/978-3-642-30428-6_6
PAISI
Keywords
Field
DocType
blog content,hybrid system,emotional distress tendency,available blogs,system architecture,deeper feeling,affect analysis,hand-crafted model,fine level,needy people,online detection,public health,depression,machine learning
Public health,Data mining,Distress,Computer science,Level of detail,Human judgment,Systems architecture,Hybrid system,Feeling
Conference
Citations 
PageRank 
References 
8
0.66
21
Authors
4
Name
Order
Citations
PageRank
Tim M. H. Li1192.34
Michael Chau2147197.79
Paul W. C. Wong381.34
Paul S. F. Yip4101.83