Abstract
Political discourse datasets are important for gaining political insights, analyzing communication strategies or social science phenomena. Although numerous political discourse corpora exist, comprehensive, high-quality, annotated datasets are scarce. This is largely due to the substantial manual effort, multidisciplinarity, and expertise required for the nuanced annotation of rhetorical strategies and ideological contexts. In this paper, we present AgoraSpeech, a meticulously curated, high-quality dataset of 171 political speeches from six parties during the Greek national elections in 2023. The dataset includes annotations (per paragraph) for six natural language processing (NLP) tasks: text classification, topic identification, sentiment analysis, named entity recognition, polarization and populism detection. A two-step annotation was employed, starting with ChatGPT-generated annotations and followed by exhaustive human-in-the-loop validation. The dataset was initially used in a case study to provide insights during the pre-election period. However, it has general applicability by serving as a rich source of information for political and social scientists, journalists, or data scientists, while it can be used for benchmarking and fine-tuning NLP and large language models (LLMs).
Abstract (translated)
政治话语数据集对于获取政治见解、分析沟通策略或社会科学研究现象至关重要。尽管存在大量政治话语语料库，但全面且高质量的标注数据集却十分稀缺。这主要是因为对修辞策略和意识形态背景进行细致标注需要巨大的人工努力、多学科知识以及专业知识。 在本文中，我们介绍了AgoraSpeech，这是一个精心整理的数据集，包含2023年希腊全国选举期间六党派的171份政治演讲文本。该数据集包含了针对六个自然语言处理（NLP）任务的标注（每段落为单位）：文本分类、主题识别、情感分析、命名实体识别、极化和民粹主义检测。 为了构建这个数据集，我们采用了两步注释方法：首先利用ChatGPT生成初步注释，然后进行详尽的人工验证。最初，该数据集被用于案例研究，在选举前提供见解。然而，它具有广泛的适用性，可作为政治和社会科学家、记者或数据科学家的信息丰富来源，并可用于基准测试和微调NLP和大型语言模型（LLMs）。
URL
https://arxiv.org/abs/2501.06265
