Language-Specific Sentiment Polarity Biases in Encoder and Large Language Model Classification of Product Reviews

• URL: http://arxiv.org/abs/2606.22745v1
• Date: Mon, 22 Jun 2026 01:16:04 GMT
• Title: Language-Specific Sentiment Polarity Biases in Encoder and Large Language Model Classification of Product Reviews
• Abstract summary: 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.<n>These language-specific polarity biases have implications in both social and business domains deploying multilingual sentiment analysis systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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