The Moral Foundations Weibo Corpus
This provides a resource for researchers analyzing moral sentiments in Chinese social media, though it is incremental as it extends existing annotation frameworks to a new language domain.
The authors tackled the lack of a large, annotated dataset for measuring moral sentiments in Chinese natural language processing by introducing the Moral Foundation Weibo Corpus, which consists of 25,671 manually annotated Weibo comments across ten moral categories, and they reported baseline classification results using large language models.
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.