2 research outputs found
Affective Conditioning on Hierarchical Networks applied to Depression Detection from Transcribed Clinical Interviews
In this work we propose a machine learning model for depression detection
from transcribed clinical interviews. Depression is a mental disorder that
impacts not only the subject's mood but also the use of language. To this end
we use a Hierarchical Attention Network to classify interviews of depressed
subjects. We augment the attention layer of our model with a conditioning
mechanism on linguistic features, extracted from affective lexica. Our analysis
shows that individuals diagnosed with depression use affective language to a
greater extent than not-depressed. Our experiments show that external affective
information improves the performance of the proposed architecture in the
General Psychotherapy Corpus and the DAIC-WoZ 2017 depression datasets,
achieving state-of-the-art 71.6 and 68.6 F1 scores respectively
Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media
Social networks enable people to interact with one another by sharing
information, sending messages, making friends, and having discussions, which
generates massive amounts of data every day, popularly called as the
user-generated content. This data is present in various forms such as images,
text, videos, links, and others and reflects user behaviours including their
mental states. It is challenging yet promising to automatically detect mental
health problems from such data which is short, sparse and sometimes poorly
phrased. However, there are efforts to automatically learn patterns using
computational models on such user-generated content. While many previous works
have largely studied the problem on a small-scale by assuming uni-modality of
data which may not give us faithful results, we propose a novel scalable hybrid
model that combines Bidirectional Gated Recurrent Units (BiGRUs) and
Convolutional Neural Networks to detect depressed users on social media such as
Twitter-based on multi-modal features. Specifically, we encode words in user
posts using pre-trained word embeddings and BiGRUs to capture latent
behavioural patterns, long-term dependencies, and correlation across the
modalities, including semantic sequence features from the user timelines
(posts). The CNN model then helps learn useful features. Our experiments show
that our model outperforms several popular and strong baseline methods,
demonstrating the effectiveness of combining deep learning with multi-modal
features. We also show that our model helps improve predictive performance when
detecting depression in users who are posting messages publicly on social
media.Comment: 23 Page