Eradicating Social Biases in Sentiment Analysis using Semantic Blinding and Semantic Propagation Graph Neural Networks
This addresses the issue of bias in sentiment analysis for applications requiring fair emotion prediction, offering a more interpretable and less biased alternative to deep learning models.
The paper tackles the problem of social biases in sentiment analysis by introducing the Semantic Propagation Graph Neural Network (SProp GNN), which uses syntactic structures and word-level emotional cues to predict emotions while being robust to biases like political or gender bias. It shows superior performance to lexicon-based alternatives such as VADER and EmoAtlas across two languages and approaches transformer-based model accuracy with reduced bias.
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.