Skip to content
😔 😞 😣 😖 😩 Detect depression on social media using the ssToT method introduced in our ASONAM 2017 paper titled "Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media"
Jupyter Notebook
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.gitignore
Depression-Detector.ipynb
LICENSE Initial commit Sep 14, 2017
README.md Update README.md Feb 24, 2019
depression_lexicon.json release commit Sep 14, 2017

README.md

Knoesis Depression Project Logo

Social-media Depression Detector (SDD)

This tool allows you to detect depression on social media using the ssToT method introduced in our ASONAM 2017 paper titled Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

HOW TO USE

Follow the steps in the Jupyter Notebook

Licenses

This work is licensed under GPL-3.0 and CreativesForGood licenses. A copy of the first license can be found in this repository. The other license can be found over this link C4G License.

GPLv3 Logo CreativesForGood Logo

Citing

If you do make use of SDD, the depression lexicon, or any of its components please cite the following publication:

Amir Hossein Yazdavar, Hussein S. Al-Olimat, Monireh Ebrahimi, Goonmeet Bajaj, Tanvi Banerjee, Krishnaprasad Thirunarayan, Jyotishman Pathak, and Amit Sheth. 2017. Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 (ASONAM '17), Jana Diesner, Elena Ferrari, and Guandong Xu (Eds.). ACM, New York, NY, USA, 1191-1198. DOI: https://doi.org/10.1145/3110025.3123028

You can’t perform that action at this time.