chanapapan/Depression-Detection
Comparing Selective Masking Methods for Depression Detection in Social Media
This project helps mental health researchers and social scientists identify individuals at risk for depression by analyzing their social media posts. It takes social media text data as input and provides a classification model that can predict the likelihood of depression, even when common keywords aren't present. The primary users are researchers focused on mental health studies using linguistic analysis.
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Use this if you are a researcher or mental health professional looking to build or evaluate models for depression detection from social media text, and you need a robust model that learns context rather than just keywords.
Not ideal if you need a clinical diagnostic tool or are looking for real-time intervention systems without further development.
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Python
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Last pushed
Jun 03, 2023
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