Integrating Natural Language Processing Techniques of Text Mining Into Financial System: Applications and Limitations

TL;DR

This paper reviews how natural language processing and text mining techniques have been applied in the financial sector from 2018 to 2023, highlighting common methods, focus areas, and challenges for future integration.

Contribution

It provides a comprehensive overview of NLP applications in finance, identifies prevalent algorithms and models, and discusses key challenges and future directions for research and implementation.

Findings

Most research combines probabilistic and vector-space models.

LSTM and bidirectional encoder models are the most used algorithms.

Focus is mainly on asset pricing within the financial system.

Abstract

The financial sector, a pivotal force in economic development, increasingly uses the intelligent technologies such as natural language processing to enhance data processing and insight extraction. This research paper through a review process of the time span of 2018-2023 explores the use of text mining as natural language processing techniques in various components of the financial system including asset pricing, corporate finance, derivatives, risk management, and public finance and highlights the need to address the specific problems in the discussion section. We notice that most of the research materials combined probabilistic with vector-space models, and text-data with numerical ones. The most used technique regarding information processing is the information classification technique and the most used algorithms include the long-short term memory and bidirectional encoder models. The research noticed that new specific algorithms are developed and the focus of the financial system is mainly on asset pricing component. The research also proposes a path from engineering perspective for researchers who need to analyze financial text. The challenges regarding text mining perspective such as data quality, context-adaption and model interpretability need to be solved so to integrate advanced natural language processing models and techniques in enhancing financial analysis and prediction. Keywords: Financial System (FS), Natural Language Processing (NLP), Software and Text Engineering, Probabilistic, Vector-Space, Models, Techniques, TextData, Financial Analysis.