用认知语言特征提升抑郁文本检测准确率
Cognitive-Linguistic Indicators of Depression in Online Communities: Analysed by DistilBERT and Holographic Reduced Representation
- 结合认知扭曲特征与DistilBERT嵌入，构建混合模型
- F1得分提升至0.94，AUC达0.981，显著优于基线
- 适合心理健康监测与自然语言处理研究者参考
本研究探讨将基于认知理论的语言特征与基于Transformer的嵌入相结合，是否能提升在线文本中抑郁的自动化检测效果。依据贝克抑郁认知理论，从抑郁相关及对照社区的Reddit帖子中提取可量化的认知扭曲特征，包括第一人称代词密度、绝对化词汇和负面情绪词。使用Kaggle Reddit自杀与抑郁检测数据集的一个子集，对比两种分类流程：以TF-IDF加朴素贝叶斯为基线，以及将DistilBERT句向量与编码认知语言特征的全息缩减表示（HRR）向量拼接后，通过逻辑回归分类的混合模型。混合模型在5折交叉验证下，宏平均F1从0.83提升至0.92，整体F1达到0.94，AUC从0.958提升至0.981。
原文摘要 · Abstract (English)
This paper investigates whether combining cognitively grounded linguistic features with transformer-based embeddings improves automated detection of depression in online text. Using Beck's Cognitive Theory of Depression, the study extracts cognitive distortions as measurable features, including first-person pronoun density, absolutist words, and negative emotion in Reddit posts from depression-related and control communities. Using a subset of the Kaggle Reddit Suicide and Depression Detection dataset, two classification pipelines are compared, a TF-IDF embedding with Naive Bayes as a baseline, and a hybrid model that concatenates DistilBERT sentence embeddings with Holographic Reduced Representation (HRR) vectors encoding the cognitive-linguistic features, followed by Logistic Regression. The hybrid DistilBERT HRR model achieves a macro F1 score of 0.94 versus 0.80 for the TD-IDF baseline, with 5-fold cross validation F1 improving from 0.83 to 0.92, and AUC from 0.958 to 0.981.
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