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This study investigates utilizing Bidirectional Encoder Representations from Transformers(BERT) models for detecting depression indicators through text classification. BERT has demonstrated impressive performance in natural language processing tasks like classification and sentiment analysis. The research emphasizes fine-tuning pre-trained BERT models, optimizing hyperparameters, modifying layer architectures, and employing ensemble techniques to enhance text classification accuracy for depression detection. The findings explore BERT’s potential to revolutionize text analysis for addressing mental health challenges.

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Fine-Tuning and Efficacy Assessment of BERT-Based Models in Detecting Early Signs of Depression

  • 会议论文

  • 发表日期：

    2025年

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  • Vaibhav Kumar,
  • Vibhav Prakash Singh

摘要

This study investigates utilizing Bidirectional Encoder Representations from Transformers(BERT) models for detecting depression indicators through text classification. BERT has demonstrated impressive performance in natural language processing tasks like classification and sentiment analysis. The research emphasizes fine-tuning pre-trained BERT models, optimizing hyperparameters, modifying layer architectures, and employing ensemble techniques to enhance text classification accuracy for depression detection. The findings explore BERT’s potential to revolutionize text analysis for addressing mental health challenges.