HEDL: Deep learning multiple approaches for early detection of depression using sarcastic text

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Computational Linguistics

Background:

• Sarcasm detection is vital for identifying mental health indicators, particularly depression.
• Traditional models (SVC, DT, RF, LR) have limitations in capturing complex sarcasm patterns.
• Existing approaches often lack robustness and accuracy in feature representation.

Purpose of the Study:

• To propose a novel Hybrid Ensemble Deep Learning (HEDL) model for enhanced sarcasm detection.
• To address the weaknesses of traditional machine learning models in identifying subtle linguistic cues.
• To improve the accuracy and reliability of sarcasm detection for applications like mental health monitoring.

Main Methods:

• Developed a unique combination of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU).
• Integrated CNN, LSTM, and GRU architectures into a single framework for comprehensive feature extraction.
• Evaluated the HEDL model on a news headline dataset.

Main Results:

• The HEDL model achieved 84% accuracy on the news headline dataset.
• Demonstrated a marked reduction in false positives compared to baseline models.
• Showcased improved accuracy and recall, indicating enhanced performance.

Conclusions:

• The Hybrid Ensemble Deep Learning (HEDL) model is a more accurate and reliable methodology for sarcasm detection.
• HEDL offers improved feature representation, robustness, and accuracy over traditional methods.
• The model's scalability suggests potential applications in mental health monitoring and sentiment analysis.