发现中英文模型对正负评价的识别存在语言差异，影响跨国舆情分析。
Language-Specific Sentiment Polarity Biases in Encoder and Large Language Model Classification of Product Reviews
- 对比不同语言下编码器与大模型的极性分类偏差
- 法语中大模型更准识别负面评论，日语编码器漏判间接批评
- 适合多语言情感分析系统设计者参考
本研究探究了在跨语言场景下，人工智能模型在判断产品评论情感极性时的系统性偏差。结果显示，大语言模型在法语评论中呈现负面偏见，对负面评价识别更准确；而编码器模型在日语中则表现出正面偏见，容易忽略使用间接批评的负面评论。这些语言特定的情感极性偏差，对依赖多语言情感分析的社会监控与商业决策系统具有重要影响。
原文摘要 · Abstract (English)
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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