Language-Specific Sentiment Polarity Biases in Encoder and Large Language Model Classification of Product Reviews
Advita Rajiv, Kavitha Kothur, Gautham Reddy
cs.CL
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.
Topics
Classified with taxonomy v2 on Wed, 2 Sept 2026.