Thematic Analysis of Big Data in Financial Institutions Using NLP Techniques with a Cloud Computing Perspective: A Systematic Literature Review

Abstract

This literature review explores the existing work and practices in applying thematic analysis natural language processing techniques to financial data in cloud environments. This work aims to improve two of the five Vs of the big data system. We used the PRISMA approach (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for the review. We analyzed the research papers published over the last 10 years about the topic in question using a keyword-based search and bibliometric analysis. The systematic literature review was conducted in multiple phases, and filters were applied to exclude papers based on the title and abstract initially, then based on the methodology/conclusion, and, finally, after reading the full text. The remaining papers were then considered and are discussed here. We found that automated data discovery methods can be augmented by applying an NLP-based thematic analysis on the financial data in cloud environments. This can help identify the correct classification/categorization and measure data quality for a sentiment analysis.

Automating financial reporting with natural language processing: A review and case analysis

“…Thematic analysis, another cornerstone NLP technique, has been effectively applied to financial data within cloud computing environments, as discussed by Sharma et al (2023). This approach involves identifying and analyzing themes or patterns within text data, facilitating a deeper understanding of financial narratives.…”