Eradicating Social Biases in Sentiment Analysis using Semantic Blinding and Semantic Propagation Graph Neural Networks

TL;DR

This paper presents SProp GNN, a novel sentiment analysis model that reduces social biases by using semantic blinding and graph neural networks, achieving high accuracy and improved interpretability across languages.

Contribution

The paper introduces the Semantic Propagation Graph Neural Network (SProp GNN), a new architecture that minimizes social biases in sentiment analysis while maintaining competitive accuracy.

Findings

SProp GNN outperforms lexicon-based methods like VADER and EmoAtlas.

It approaches transformer-based model accuracy.

It significantly reduces social biases in emotion prediction.

Abstract

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.