通过屏蔽敏感词汇信息，实现更公平的情感分析。
Eradicating Social Biases in Sentiment Analysis using Semantic Blinding and Semantic Propagation Graph Neural Networks
- 仅依赖语法结构和词级情绪线索进行情感判断。
- 在双语任务中性能优于VADER与EmoAtlas，接近Transformer模型。
- 减少政治与性别偏见，适合追求公平性的人类行为分析场景。
本文提出语义传播图神经网络（SProp GNN），一种仅依赖句法结构和词级情感线索进行文本情感预测的机器学习架构。通过语义屏蔽特定词汇信息，该模型对政治、性别等社会偏见具有鲁棒性。在两个不同预测任务及两种语言上，SProp GNN的表现优于VADER和EmoAtlas等词典方法，并接近基于Transformer的模型性能，同时显著降低情感预测中的偏差。该模型兼具可解释性与强表达能力，弥合了可解释词典方法与黑箱深度学习模型之间的方法鸿沟，为文本中人类行为的情绪分析提供了一种公平且高效的新工具。
原文摘要 · Abstract (English)
This paper introduces the Semantic Propagation Graph Neural Network (SProp GNN), a machine learning sentiment analysis (SA) architecture that relies exclusively on syntactic structures and word-level emotional cues to predict emotions in text. By semantically blinding the model to information about specific words, it is robust to social biases such as political or gender bias that have been plaguing previous machine learning-based SA systems. The SProp GNN shows performance superior to lexicon-based alternatives such as VADER (Valence Aware Dictionary and Sentiment Reasoner) and EmoAtlas on two different prediction tasks, and across two languages. Additionally, it approaches the accuracy of transformer-based models while significantly reducing bias in emotion prediction tasks. By offering improved explainability and reducing bias, the SProp GNN bridges the methodological gap between interpretable lexicon approaches and powerful, yet often opaque, deep learning models, offering a robust tool for fair and effective emotion analysis in understanding human behavior through text.
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