Inter and Intra Document Attention for Depression Risk Assessment
We take interest in the early assessment of risk for depression in social media users. We focus on the eRisk 2018 dataset, which represents users as a sequence of their written online contributions. We implement four RNN-based systems to classify the users. We explore several aggregations methods to combine predictions on individual posts. Our best model reads through all writings of a user in parallel but uses an attention mechanism to prioritize the most important ones at each timestep.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Deep Bag-of-Sub-Emotions for Depression Detection in Social Media
This paper presents the Deep Bag-of-Sub-Emotions (DeepBoSE), a novel deep learning model for depression detection in social media. The model is formulated such that it internally computes a differentiable Bag-of-Features…
Deep LearningDepression DetectionProbabilistic Deep LearningTransfer LearningPsychiatric Scale Guided Risky Post Screening for Early Detection of Depression
Depression is a prominent health challenge to the world, and early risk detection (ERD) of depression from online posts can be a promising technique for combating the threat. Early depression detection faces the challeng…
Depression DetectionDiagnosticReDepress: A Cognitive Framework for Detecting Depression Relapse from Social Media
Almost 50% depression patients face the risk of going into relapse. The risk increases to 80% after the second episode of depression. Although, depression detection from social media has attained considerable attention, …
Multi-level Attention network using text, audio and video for Depression Prediction
Depression has been the leading cause of mental-health illness worldwide. Major depressive disorder (MDD), is a common mental health disorder that affects both psychologically as well as physically which could lead to lo…
Decision MakingDiagnosticTowards More Efficient Depression Risk Recognition via Gait
Depression, a highly prevalent mental illness, affects over 280 million individuals worldwide. Early detection and timely intervention are crucial for promoting remission, preventing relapse, and alleviating the emotiona…