Depression Detection

5 papers with code • 2 benchmarks • 1 datasets

Depression Detection is the problem of identifying signs of depression in individuals. These signs might be identified in peoples’ speech, facial expressions and in the use of language.

Source: Affective Conditioning on Hierarchical Attention Networks applied to Depression Detection from Transcribed Clinical Interviews

Datasets


Greatest papers with code

A Text Classification Framework for Simple and Effective Early Depression Detection Over Social Media Streams

sergioburdisso/pyss3 18 May 2019

With the rise of the Internet, there is a growing need to build intelligent systems that are capable of efficiently dealing with early risk detection (ERD) problems on social media, such as early depression detection, early rumor detection or identification of sexual predators.

Depression Detection General Classification +1

Text-based depression detection on sparse data

richermans/text_based_depression 8 Apr 2019

Previous text-based depression detection is commonly based on large user-generated data.

Depression Detection Word Embeddings

Gender Bias in Depression Detection Using Audio Features

adbailey1/DepAudioNet_reproduction 28 Oct 2020

Depression is a large-scale mental health problem and a challenging area for machine learning researchers in detection of depression.

Depression Detection

Cost-sensitive Boosting Pruning Trees for depression detection on Twitter

BIPL-UoL/Cost-Boosting-Pruning-Trees-for-depression-detection-on-Twitter 2 Jun 2019

Depression is one of the most common mental health disorders, and a large number of depressed people commit suicide each year.

Classification Depression Detection +1

Affective Conditioning on Hierarchical Networks applied to Depression Detection from Transcribed Clinical Interviews

danaiksez/depression-detection 4 Jun 2020

In this work we propose a machine learning model for depression detection from transcribed clinical interviews.

Depression Detection