CapsF: Capsule Fusion for Extracting psychiatric stressors for suicide from twitter
February 07, 2024 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Mohammad Ali Dadgostarnia, Ramin Mousa, Saba Hesaraki
arXiv ID
2403.15391
Category
cs.CL: Computation & Language
Cross-listed
cs.SI
Citations
0
Venue
arXiv.org
Last Checked
6 months ago
Abstract
Along with factors such as cancer, blood pressure, street accidents and stroke, suicide has been one of Iran main causes of death. One of the main reasons for suicide is psychological stressors. Identifying psychological stressors in an at risk population can help in the early prevention of suicidal and suicidal behaviours. In recent years, the widespread popularity and flow of real time information sharing of social media have allowed for potential early intervention in large scale and even small scale populations. However, some automated approaches to extract psychiatric stressors from Twitter have been presented, but most of this research has been for non Persian languages. This study aims to investigate the techniques of detecting psychological stress related to suicide from Persian tweets using learning based methods. The proposed capsule based approach achieved a binary classification accuracy of 0.83.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Computation & Language
๐
๐
Old Age
๐
๐
Old Age
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
๐
๐
Old Age
XLNet: Generalized Autoregressive Pretraining for Language Understanding
๐ฎ
๐ฎ
The Ethereal
Effective Approaches to Attention-based Neural Machine Translation
๐
๐
Old Age
A large annotated corpus for learning natural language inference
๐
๐
Old Age
HellaSwag: Can a Machine Really Finish Your Sentence?
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
๐ป
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
๐ป
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
๐ป
Ghosted