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 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 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.

翻译：本文介绍了语义传播图神经网络（SProp GNN），这是一种机器学习情感分析架构，其完全依赖句法结构和词级情感线索来预测文本中的情感。通过使模型在语义上对特定词语的信息“失明”，该模型对困扰以往基于机器学习的情感分析系统的偏见（如政治或性别偏见）具有鲁棒性。SProp GNN 在两项不同的预测任务以及跨两种语言的测试中，其性能均优于基于词典的替代方案（如 VADER 和 EmoAtlas）。此外，它在情感预测任务中接近基于 Transformer 的模型的准确度，同时显著降低了偏见。通过提供更好的可解释性并减少偏见，SProp GNN 弥合了可解释的词典方法与强大但通常不透明的深度学习模型之间的方法论鸿沟，为通过文本来理解人类行为提供了一个公平且有效的情感分析工具。