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A Novel Text Mining Approach for Mental Health Prediction Using Bi-LSTM and BERT Model.

Kamil Zeberga1, Muhammad Attique2, Babar Shah3

  • 1Department of Artificial Intelligence, Ajou University, Suwon, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces a novel framework for detecting depression and anxiety on social media using Bidirectional Encoder Representations from Transformers (BERT) and knowledge distillation. The system achieves 98% accuracy, improving upon existing methods for mental health detection.

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Area of Science:

  • Computational linguistics
  • Artificial Intelligence
  • Social media analytics

Background:

  • Growing demand for intelligent systems to detect mental health issues like depression and anxiety on social media.
  • Existing machine learning and deep learning models struggle with unstructured social media text, lacking long-term dependencies and proper exploitation of advanced schemes.
  • Need for improved text representation and deep learning techniques for accurate health-related problem detection.

Purpose of the Study:

  • To propose a novel framework for efficient and effective identification of depression and anxiety-related posts on social media.
  • To maintain the contextual and semantic meaning of words within the entire corpus using Bidirectional Encoder Representations from Transformers (BERT).
  • To enhance model performance and accuracy through knowledge distillation from a large pre-trained BERT model to a smaller one.

Main Methods:

  • Developed a novel framework utilizing Bidirectional Encoder Representations from Transformers (BERT) for text analysis.
  • Implemented a knowledge distillation technique to transfer knowledge from BERT to a smaller model, boosting performance.
  • Created a custom data collection framework from social media platforms (Reddit and Twitter).
  • Employed word2vec and BERT with Bi-LSTM for analyzing and detecting depression and anxiety signs.

Main Results:

  • The proposed system effectively identifies depression and anxiety signs from social media posts.
  • Achieved a high accuracy of 98% by leveraging the knowledge distillation technique.
  • The framework successfully maintains the contextual and semantic meaning of words in social media text.

Conclusions:

  • The novel framework demonstrates superior performance compared to state-of-the-art methods for mental health detection on social media.
  • BERT combined with knowledge distillation offers a powerful approach for analyzing complex social media data for health-related issues.
  • The system provides an accurate and efficient solution for identifying depression and anxiety through social media post analysis.