Exploiting temporal information in a two-stage classification framework for content-based depression detection
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Journal Volume
7818 LNAI
Journal Issue
PART 1
Pages
276-288
Date Issued
2013
Author(s)
Abstract
Depression has become a critical illness in human society as many people suffer from the condition without being aware of it. The goal of this paper is to design a system to identify potential depression candidates based on their write-ups. To solve this problem, we propose a two-stage supervised learning framework. The first stage determines whether the user possesses apparent negative emotion. Then the positive cases are passed to the second stage to further evaluate whether the condition is clinical depression or just ordinary sadness. Our training data are generated automatically from Bulletin Board Systems. The content and temporal features are designed to improve the classification accuracy. Finally we develop an online demo system that takes a piece of written text as input, and outputs the likelihood of the author currently suffering depression. We conduct cross-validation and human study to evaluate the effectiveness of this system. ? Springer-Verlag Berlin Heidelberg 2013.
SDGs
Other Subjects
Bulletin board systems; Classification accuracy; Classification framework; Clinical depression; Potential depression; Temporal information; Text classification; Time information; Bulletin boards; Classification (of information); Text processing; Data mining
Type
conference paper