Mitigating social bias in sentiment classification via ethnicity-aware algorithmic design

Abstract Sentiment analysis tools are frequently employed to analyze large amounts of natural language data gathered from social networks and generate valuable insights on public opinion. Research has discovered that these tools tend to be biased against some demographic groups, based on social attributes such as gender, age, and ethnicity. Sentiment classification works dealt with this issue by means of data balancing and algorithmic approaches. However, one crucial limitation of existing methods is the inability to tackle social bias while maintaining satisfactory model performance. In this paper, we aim to fill this gap by proposing a sentiment classification method that entails ethnicity-aware algorithmic design. Specifically, our method involves balanced training and a custom ethnicity-aware loss function that leverages ethnicity group information to foster a fair model optimization process. The proposed loss incentivizes the model to iteratively improve accuracy for currently underperforming demographic or social groups, therefore simultaneously decreasing social bias and boosting overall performance. Our extensive qualitative and quantitative experimental evaluation involving a large corpus of user reviews demonstrated the effectiveness of the proposed method, also when compared to popular baselines for sentiment classification.