🤖 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.
📝 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.
