用算法自动化文献综述，提升金融叙事研究的系统性与可复现性。
An Algorithmic Framework for Systematic Literature Reviews: A Case Study for Financial Narratives
- 结合NLP与聚类技术，自动筛选和分析学术文献。
- 发现金融叙事研究多依赖情感分析，缺乏统一理论框架。
- 适合需要高效整理前沿文献的研究者或政策制定者。
本文提出一种算法化文献综述框架，旨在提升文献回顾过程中的效率、可复现性与选择质量评估。该方法融合自然语言处理（NLP）、聚类算法与可解释性工具，实现学术出版物的自动化筛选与分析。以金融叙事这一新兴领域为案例研究，聚焦由个体解读汇聚形成的经济事件结构化描述如何影响市场动态与资产价格。基于Scopus数据库的同行评审文献，研究揭示了当前利用多种NLP技术建模金融叙事的努力。结果显示，尽管已有进展，但金融叙事的理论建构仍碎片化，常被简化为情感分析、主题建模或其组合，缺乏统一理论框架。研究强调更严谨、动态的叙事建模方法的价值，并验证了所提算法化文献综述方法的有效性。
原文摘要 · Abstract (English)
This paper introduces an algorithmic framework for conducting systematic literature reviews (SLRs), designed to improve efficiency, reproducibility, and selection quality assessment in the literature review process. The proposed method integrates Natural Language Processing (NLP) techniques, clustering algorithms, and interpretability tools to automate and structure the selection and analysis of academic publications. The framework is applied to a case study focused on financial narratives, an emerging area in financial economics that examines how structured accounts of economic events, formed by the convergence of individual interpretations, influence market dynamics and asset prices. Drawing from the Scopus database of peer-reviewed literature, the review highlights research efforts to model financial narratives using various NLP techniques. Results reveal that while advances have been made, the conceptualization of financial narratives remains fragmented, often reduced to sentiment analysis, topic modeling, or their combination, without a unified theoretical framework. The findings underscore the value of more rigorous and dynamic narrative modeling approaches and demonstrate the effectiveness of the proposed algorithmic SLR methodology.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。