可比文档中的跨语言观点与情感挖掘
Cross-lingual Opinions and Emotions Mining in Comparable Documents
AI总结：
本研究通过构建双语情绪词典和跨语言标注方法，分析英阿可比文档中的情感与情绪差异，发现同源新闻机构文档标注一致，异源则分歧，方法具有语言无关性。
摘要：
可比文本是多种语言中主题对齐但并非直接翻译的文档。它们对于理解一个主题在不同语言中如何被讨论具有重要价值。本研究探讨了英阿可比文档中情感与情绪表达的差异。首先，对文本进行情感和情绪标签的标注。我们采用一种跨语言方法为文档标注观点类别（主观/客观），从而避免依赖机器翻译。为了进行情绪（愤怒、厌恶、恐惧、喜悦、悲伤、惊讶）的标注，我们将英文WordNet-Affect（WNA）词典手动翻译成阿拉伯语，创建了用于标注可比语料库的双语情绪词典。随后，我们应用一种统计度量来评估每个源-目标文档对中情感和情绪的一致性。当文档来自不同来源时，这种比较尤为相关。据我们所知，这一方面在以往文献中尚未被探讨。我们的研究包括了来自欧洲新闻台、英国广播公司和半岛电视台（JSC）的英阿文档对。结果表明，当文章来自同一新闻机构时，情感和情绪标注趋于一致，而来自不同机构时则存在分歧。所提出的方法具有语言无关性，并可推广到其他语言对。
英文摘要：
Comparable texts are topic-aligned documents in multiple languages that are not direct translations. They are valuable for understanding how a topic is discussed across languages. This research studies differences in sentiments and emotions across English-Arabic comparable documents. First, texts are annotated with sentiment and emotion labels. We apply a cross-lingual method to label documents with opinion classes (subjective/objective), avoiding reliance on machine translation. To annotate with emotions (anger, disgust, fear, joy, sadness, surprise), we manually translate the English WordNet-Affect (WNA) lexicon into Arabic, creating bilingual emotion lexicons used to label the comparable corpora. We then apply a statistical measure to assess the agreement of sentiments and emotions in each source-target document pair. This comparison is especially relevant when the documents originate from different sources. To our knowledge, this aspect has not been explored in prior literature. Our study includes English-Arabic document pairs from Euronews, BBC, and Al-Jazeera (JSC). Results show that sentiment and emotion annotations align when articles come from the same news agency and diverge when they come from different ones. The proposed method is language-independent and generalizable to other language pairs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
