用大模型分析小说角色性格，支持一键标注与验证
LLM_annotate: A Python package for annotating and analyzing fiction characters
- 基于大模型自动标注角色行为与性格特征
- 支持文本分块、姓名消歧和质量评分，结果可复现
- 适合文学研究者快速开展角色分析，兼容各类大模型
LLM_annotate 是一个用于利用大语言模型分析虚构角色人格的 Python 工具包。它统一了在完整文本（如书籍和电影剧本）中对角色行为进行标注、推断角色特质，并通过人机交互界面验证标注与推断质量的工作流程。该工具包包含文本分块、基于大模型的标注、角色名称消歧、质量评分，以及角色级统计与嵌入计算等功能。研究人员可在 LLM_annotate 中使用任意大模型，包括商业、开源或自定义模型。通过《辛普森一家电影》和小说《傲慢与偏见》的教程示例，展示了该工具包在高效、可复现的角色分析中的应用。
原文摘要 · Abstract (English)
LLM_annotate is a Python package for analyzing the personality of fiction characters with large language models. It standardizes workflows for annotating character behaviors in full texts (e.g., books and movie scripts), inferring character traits, and validating annotation/inference quality via a human-in-the-loop GUI. The package includes functions for text chunking, LLM-based annotation, character name disambiguation, quality scoring, and computation of character-level statistics and embeddings. Researchers can use any LLM, commercial, open-source, or custom, within LLM_annotate. Through tutorial examples using The Simpsons Movie and the novel Pride and Prejudice, I demonstrate the usage of the package for efficient and reproducible character analyses.
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