用积分梯度分析文本中影响心理特征判断的关键词汇
Application of integrated gradients explainability to sociopsychological semantic markers
- 通过积分梯度方法定位影响分类的关键词
- 在小样本下仍能识别出关键语义标记词
- 结果符合社会心理学理论，适合人文研究者使用
针对文本分类中情感及更细微的社会心理标记（如自主性）的识别需求，本文采用积分梯度（IG）方法，将分类结果追溯至词级别，揭示哪些词语真正影响判断。研究聚焦于目前仅有验证性深度学习模型BERTAgent可用的自主性标记，系统测试了性能与参数设置，评估了替代方法，并在实际应用中验证了结果的有效性。在仅有少量标注数据的情况下，采用鼓励过拟合的特殊训练策略，增强各类别间的区分度，以识别与社会心理标记相关的显著词汇。结果从社会心理学视角进行分析，提供了有价值的洞察。
原文摘要 · Abstract (English)
Classification of textual data in terms of sentiment, or more nuanced sociopsychological markers (e.g., agency), is now a popular approach commonly applied at the sentence level. In this paper, we exploit the integrated gradient (IG) method to capture the classification output at the word level, revealing which words actually contribute to the classification process. This approach improves explainability and provides in-depth insights into the text. We focus on sociopsychological markers beyond sentiment and investigate how to effectively train IG in agency, one of the very few markers for which a verified deep learning classifier, BERTAgent, is currently available. Performance and system parameters are carefully tested, alternatives to the IG approach are evaluated, and the usefulness of the result is verified in a relevant application scenario. The method is also applied in a scenario where only a small labeled dataset is available, with the aim of exploiting IG to identify the salient words that contribute to building the different classes that relate to relevant sociopsychological markers. To achieve this, an uncommon training procedure that encourages overfitting is employed to enhance the distinctiveness of each class. The results are analyzed through the lens of social psychology, offering valuable insights.
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