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halolimat / Social-media-Depression-Detector

:pensive: :disappointed: :persevere: :confounded: :weary: 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"
GNU General Public License v3.0
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depression detection lda lexicon machine-learning nlp phq9 semi-supervised-learning social-media

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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.

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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