An Algorithmic Framework for Systematic Literature Reviews: A Case Study for Financial Narratives

2026-01-07

AI Summary

Traditional systematic literature reviews suffer from low efficiency and poor reproducibility, particularly when synthesizing fragmented research in emerging fields such as financial narrative. To address this limitation, this study proposes an algorithmic review framework that integrates natural language processing, clustering, and explainable AI techniques to automatically retrieve and structurally analyze scholarly publications from the Scopus database. Applying this framework to the domain of financial narrative—a first for the field—the analysis reveals a predominant focus on sentiment analysis and topic modeling, yet a notable absence of theoretical integration. The results demonstrate that the proposed approach significantly enhances the efficiency, quality, and reproducibility of literature reviews, thereby advancing financial narrative research toward more unified theoretical modeling.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Application Domains: Humanities & Computational Social Science

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

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.

Problem

• systematic literature review
• financial narratives
• natural language processing
• narrative modeling
• conceptual fragmentation

Innovation

• algorithmic systematic literature review
• natural language processing
• financial narratives
• clustering algorithms
• interpretability tools