从文本中提取政治网络的令人丧的简单方法
Naim Bro1,2
1School of Government, Adolfo Ibanez University, Santiago, Chile.
PloS one
|January 27, 2025
概括
GPT-4从新闻中提取政治网络,显示负面情绪与立法协议较少相关. 包括情绪分析在内显著提高了网络分析的准确性.
科学领域:
- 计算社会科学 计算社会科学
- 政治科学 政治科学是指政治学.
- 自然语言处理自然语言处理.
背景情况:
- 传统上,政治网络分析依赖于结构化数据.
- 先进的语言模型为从非结构化文本中提取复杂的关系数据提供了新的可能性.
- 对于社会科学家来说,像GPT-4这样的模型的可访问性需要探索它们在网络分析中的应用.
研究的目的:
- 证明GPT-4及其变体在从文本数据中提取政治网络的有效性.
- 将实体识别,关系提取,实体链接和情绪分析集成到一个统一的网络提取过程中.
- 使用立法协议数据验证GPT-4衍生政治网络的准确性.
主要方法:
- 使用GPT-4进行实体识别,关系提取,实体链接和情绪分析的连贯过程.
- 将该方法应用于1009篇智利政治新闻文章的集体.
- 使用立法协议 (同向投票频率) 验证的网络提取,并使用线性回归和节点嵌入进行分析.
主要成果:
- 由GPT-4确定的情绪与议员共同投票的频率一致.
- 由GPT-4预测的负关系与减少的立法协议相对应.
- 网络分析显示,当包括情绪数据时,预测能力明显更强.
结论:
- GPT-4是社会科学家用文本构建和分析政治网络的多功能工具.
- 整合情绪分析可以提高政治网络分析的准确性和预测能力.
- 这项研究验证了一种新的,凝聚力的方法,利用先进的语言模型对基于文本的政治网络进行提取.
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