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Depression is a common mental health issue today. Early detection of depression and anxiety is crucial for prevention and treatment. Given the large global population of Chinese society, research regarding Chinese depression analysis remains low. Therefore, this study utilizes the deep learning models, TextCNN, BiLSTM, and BERT, to predict depression based on user comments from Chinese social media. By adjusting the parameters and comparing the outcome of these models, it has been demonstrated that BERT is a well-suited model for predicting Chinese depression comments compared to other models.

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A Deep Learning Sentiment Analysis for Depression Detection Using Chinese Social Media Comments

  • 会议论文

  • 发表日期：

    2026年

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  • Li-Hua Li,
  • Yung-Cheng Chuang

摘要

Depression is a common mental health issue today. Early detection of depression and anxiety is crucial for prevention and treatment. Given the large global population of Chinese society, research regarding Chinese depression analysis remains low. Therefore, this study utilizes the deep learning models, TextCNN, BiLSTM, and BERT, to predict depression based on user comments from Chinese social media. By adjusting the parameters and comparing the outcome of these models, it has been demonstrated that BERT is a well-suited model for predicting Chinese depression comments compared to other models.