用AI解析500万发展项目文本，挖掘隐藏的援助意图
Cracking the Code: Enhancing Development finance understanding with artificial intelligence
- 用BERTopic模型分析项目叙述文本，自动聚类分类
- 从500万条项目描述中发现未被预设分类覆盖的新主题
- 帮助理解捐助方真实意图，适合发展政策与金融研究者
发展项目分析对理解捐助方援助策略、受援方优先事项，以及评估发展资金在实地行动中的能力至关重要。经合组织（OECD）信贷方报告系统（CRS）数据集是该领域的重要参考，包含约500万条来自不同领域的项目叙述。尽管其信息丰富，但因依赖捐助方自报主要目标和预定义行业分类，难以准确揭示项目实际目的。本研究采用机器学习技术，特别是自然语言处理（NLP）中的创新方法BERTopic，基于项目叙述文本进行主题聚类与标签生成。通过识别发展金融中潜在但隐含的主题，该AI应用提升了对捐助方优先事项和整体发展资金流向的理解，并为公共与私营项目文本分析提供了新方法。
原文摘要 · Abstract (English)
Analyzing development projects is crucial for understanding donors aid strategies, recipients priorities, and to assess development finance capacity to adress development issues by on-the-ground actions. In this area, the Organisation for Economic Co-operation and Developments (OECD) Creditor Reporting System (CRS) dataset is a reference data source. This dataset provides a vast collection of project narratives from various sectors (approximately 5 million projects). While the OECD CRS provides a rich source of information on development strategies, it falls short in informing project purposes due to its reporting process based on donors self-declared main objectives and pre-defined industrial sectors. This research employs a novel approach that combines Machine Learning (ML) techniques, specifically Natural Language Processing (NLP), an innovative Python topic modeling technique called BERTopic, to categorise (cluster) and label development projects based on their narrative descriptions. By revealing existing yet hidden topics of development finance, this application of artificial intelligence enables a better understanding of donor priorities and overall development funding and provides methods to analyse public and private projects narratives.
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