Riveter provides a complete easy-to-use pipeline for analyzing verb connotations associated with entities in text corpora. We prepopulate the package with connotation frames of sentiment, power, and agency, which have demonstrated usefulness for capturing social phenomena, such as gender bias, in a broad range of corpora. For decades, lexical frameworks have been foundational tools in computational social science, digital humanities, and natural language processing, facilitating multifaceted analysis of text corpora. But working with verb-centric lexica specifically requires natural language processing skills, reducing their accessibility to other researchers. By organizing the language processing pipeline, providing complete lexicon scores and visualizations for all entities in a corpus, and providing functionality for users to target specific research questions, Riveter greatly improves the accessibility of verb lexica and can facilitate a broad range of future research.

翻译：Riveter提供了一套完整且易于使用的分析流程，用于检测文本语料库中与实体相关的动词内涵。我们预置了情感、权力和能动性这三类内涵框架，这些框架已被证明能有效捕捉各类语料库中的社会现象（如性别偏见）。数十年来，词汇框架一直是计算社会科学、数字人文和自然语言处理领域的基础性工具，支持对文本语料库进行多维度分析。但以动词为核心的词汇分析需要自然语言处理专业技能，这降低了其他研究者的可及性。通过优化语言处理流程、提供语料库中所有实体的完整词汇评分与可视化结果，以及支持用户针对具体研究问题定制分析，Riveter显著提升了动词词汇的可访问性，为未来广泛的研究工作提供了便利。