Analyzing depression tendency of web posts using an event-driven depression tendency warning model.

Journal: Artificial intelligence in medicine

Published Date: Jan 1, 2016

Abstract

OBJECTIVE: The Internet has become a platform to express individual moods/feelings of daily life, where authors share their thoughts in web blogs, micro-blogs, forums, bulletin board systems or other media. In this work, we investigate text-mining technology to analyze and predict the depression tendency of web posts.

Authors

Chiaming Tung

Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan, ROC. Electronic address: p7897127@mail.ncku.edu.tw.
Wenhsiang Lu

Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan, ROC. Electronic address: whlu@mail.ncku.edu.tw.

Keywords

Blogging Data Mining Depressive Disorder, Major Diagnosis, Computer-Assisted Early Diagnosis Emotions Humans Natural Language Processing Pattern Recognition, Automated Predictive Value of Tests Risk Assessment Risk Factors Vocabulary

External Resources

View on PubMed Access via DOI PubMed (26616420)

Analyzing depression tendency of web posts using an event-driven depression tendency warning model.

Abstract

Authors

Keywords

External Resources

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