核心概念
Automated approach using Capsule Fusion to detect psychiatric stressors in Persian tweets.
摘要
This article discusses the use of Capsule Fusion to identify psychiatric stressors related to suicide in Persian tweets. It covers the importance of early detection and prevention of suicidal behaviors through social media analysis. The study achieved a binary classification accuracy of 0.83 using a capsule-based approach.
Structure:
- Introduction:
- Suicide as a leading cause of death in Iran.
- Importance of identifying psychiatric stressors.
- Related Works:
- Previous research on mental disorders classification.
- System Overview:
- Pipeline for identifying psychiatric stressors from Twitter.
- Methodology:
- Feature vector extraction and CapsuleNet implementation.
- Experimental Results:
- Comparison with other approaches like Bag of Words, CNN, RNN.
- Conclusion:
- Summary of findings and future improvements.
統計資料
The proposed capsule-based approach achieved a binary classification accuracy of 0.83.
引述
"Identifying risk factors, stressors, and causes of suicide is a fundamental step."
"Artificial intelligence can help identify people in crisis to intervene with emotional support."