Abstract
Summarization significantly impacts sentiment analysis across languages with diverse morphologies. This study examines extractive and abstractive summarization effects on sentiment classification in English, German, French, Spanish, Italian, Finnish, Hungarian, and Arabic. We assess sentiment shifts post-summarization using multilingual transformers (mBERT, XLM-RoBERTa, T5, and BART) and language-specific models (FinBERT, AraBERT). Results show extractive summarization better preserves sentiment, especially in morphologically complex languages, while abstractive summarization improves readability but introduces sentiment distortion, affecting sentiment accuracy. Languages with rich inflectional morphology, such as Finnish, Hungarian, and Arabic, experience greater accuracy drops than English or German. Findings emphasize the need for language-specific adaptations in sentiment analysis and propose a hybrid summarization approach balancing readability and sentiment preservation. These insights benefit multilingual sentiment applications, including social media monitoring, market analysis, and cross-lingual opinion mining.
Abstract (translated)
摘要生成显著影响具有不同形态结构的语言的情感分析。这项研究考察了抽取式和抽象式摘要对英语、德语、法语、西班牙语、意大利语、芬兰语、匈牙利语和阿拉伯语情感分类的影响。我们使用多语言转换器（mBERT，XLM-RoBERTa，T5 和 BART）以及特定语言的模型（FinBERT，AraBERT），评估摘要生成后的情感变化。结果表明，在形态复杂的语言中，抽取式摘要更好地保持了情感，而抽象式摘要虽然提高了可读性，但也引入了情感扭曲，影响了情感准确性。具有丰富屈折形态的语言，如芬兰语、匈牙利语和阿拉伯语，其准确度下降幅度比英语或德语更大。研究结果强调了在情感分析中进行语言特定适应的必要性，并提出了平衡可读性和情感保持的混合摘要方法。这些见解有利于多语言情感应用，包括社交媒体监测、市场分析和跨语言观点挖掘。
URL
https://arxiv.org/abs/2504.00265
