Moral framing and sentiment can affect a variety of online and offline behaviors, including donation, environmental action, political engagement, and protest. Various computational methods in Natural Language Processing (NLP) have been used to detect moral sentiment from textual data, but achieving strong performance in such subjective tasks requires large, hand-annotated datasets. Previous corpora annotated for moral sentiment have proven valuable, and have generated new insights both within NLP and across the social sciences, but have been limited to Twitter. To facilitate improving our understanding of the role of moral rhetoric, we present the Moral Foundations Reddit Corpus, a collection of 16,123 English Reddit comments that have been curated from 12 distinct subreddits, hand-annotated by at least three trained annotators for 8 categories of moral sentiment (i.e., Care, Proportionality, Equality, Purity, Authority, Loyalty, Thin Morality, Implicit/Explicit Morality) based on the updated Moral Foundations Theory (MFT) framework. We evaluate baselines using large language models (Llama3-8B, Ministral-8B) in zero-shot, few-shot, and PEFT settings, comparing their performance to fine-tuned encoder-only models like BERT. The results show that LLMs continue to lag behind fine-tuned encoders on this subjective task, underscoring the ongoing need for human-annotated moral corpora for AI alignment evaluation. Keywords: moral sentiment annotation, moral values, moral foundations theory, multi-label text classification, large language models, benchmark dataset, evaluation and alignment resource

翻译：道德框架与情感能够影响多种线上及线下行为，包括捐赠、环保行动、政治参与和抗议活动。自然语言处理（NLP）领域已采用多种计算方法从文本数据中检测道德情感，但在此类主观性任务中实现优异性能需要大规模人工标注数据集。以往针对道德情感标注的语料库已被证明具有重要价值，并在NLP及社会科学领域催生了新的见解，但这些语料库仅限于Twitter平台。为促进深化对道德修辞作用的理解，本文提出道德基础Reddit语料库——该数据集包含从12个不同Reddit子论坛精选的16,123条英文评论，每条评论均由至少三名训练有素的标注者基于更新的道德基础理论框架，针对8类道德情感（即关怀、比例性、平等、纯洁、权威、忠诚、浅层道德、隐式/显式道德）进行人工标注。我们使用大语言模型（Llama3-8B、Ministral-8B）在零样本、少样本和参数高效微调设置下评估基线性能，并将其与BERT等经过微调的仅编码器模型进行比较。结果表明，在此主观任务上大语言模型仍落后于微调编码器，这凸显了人工智能对齐评估持续需要人工标注道德语料库的必要性。关键词：道德情感标注、道德价值观、道德基础理论、多标签文本分类、大语言模型、基准数据集、评估与对齐资源