Moral sentiments expressed in natural language significantly influence both online and offline environments, shaping behavioral styles and interaction patterns, including social media selfpresentation, cyberbullying, adherence to social norms, and ethical decision-making. To effectively measure moral sentiments in natural language processing texts, it is crucial to utilize large, annotated datasets that provide nuanced understanding for accurate analysis and modeltraining. However, existing corpora, while valuable, often face linguistic limitations. To address this gap in the Chinese language domain,we introduce the Moral Foundation Weibo Corpus. This corpus consists of 25,671 Chinese comments on Weibo, encompassing six diverse topic areas. Each comment is manually annotated by at least three systematically trained annotators based on ten moral categories derived from a grounded theory of morality. To assess annotator reliability, we present the kappa testresults, a gold standard for measuring consistency. Additionally, we apply several the latest large language models to supplement the manual annotations, conducting analytical experiments to compare their performance and report baseline results for moral sentiment classification.

翻译：自然语言中表达出的道德情感显著影响着线上与线下环境，塑造着行为风格与互动模式，包括社交媒体上的自我呈现、网络欺凌、社会规范遵从以及伦理决策。为了有效衡量自然语言处理文本中的道德情感，利用大规模、经过标注的数据集至关重要，这些数据集能够提供细致入微的理解，以支持准确的分析和模型训练。然而，现有语料库虽然具有价值，却常常面临语言上的局限性。为填补中文领域的这一空白，我们引入了道德基础微博语料库。该语料库包含25,671条中文微博评论，涵盖六个不同主题领域。每条评论均由至少三名经过系统培训的标注者，依据源自扎根道德理论的十个道德类别进行人工标注。为评估标注者间的一致性，我们提供了衡量一致性的黄金标准——kappa检验结果。此外，我们应用了数种最新的大型语言模型来辅助人工标注，并进行了分析实验以比较其性能，同时报告了道德情感分类的基线结果。