🤖 AI Summary
This study investigates language-specific polarity biases in multilingual sentiment analysis, revealing significant and directionally divergent disparities in how AI models classify positive and negative reviews across languages. Through a systematic comparison of encoder-based models and large language models on multilingual product review datasets, the work uncovers a pronounced negative bias in French large language models and a positive bias in Japanese encoder models—attributed to culturally embedded indirect criticism in Japanese discourse. These findings demonstrate that linguistic structures and cultural context profoundly shape model behavior, offering critical empirical evidence and cautionary insights for deploying sentiment analysis systems in multilingual commercial and societal applications.
📝 Abstract
This study investigates sentiment polarity biases, specifically, differences in how accurately AI models classify positive versus negative reviews across languages and model architectures. Large language models show a negative bias in French and are more accurate on negative reviews, while encoder models exhibit positive bias in Japanese, missing negative reviews that use indirect criticism. These language-specific polarity biases have implications in both social and business domains deploying multilingual sentiment analysis systems.