Digital Text Analytics in Narrative Analysis of Financial Reports: A Computational Approach to Interpret Accounting Language

Khairul Khairul; Sulaiman Ahmad; Syahrudin Marpaung; Ratna Ratna; Jonni Hamonangan Silaen

doi:10.55927/ijbae.v4i6.440

Authors

Khairul Khairul Politeknik Negeri Medan
Sulaiman Ahmad Politeknik Negeri Medan
Syahrudin Marpaung Politeknik Negeri Medan
Ratna Ratna Politeknik Negeri Medan
Jonni Hamonangan Silaen Politeknik Negeri Medan

DOI:

https://doi.org/10.55927/ijbae.v4i6.440

Keywords:

Text Analytics, Accounting Narrative, Sentiment Analysis, Financial Reports

Abstract

Financial reports contain not only numerical data but also narratives that reflect managerial perceptions, strategies, and communications. This study aims to develop a digital text analytics approach to interpret accounting language in annual reports of companies on the Indonesia Stock Exchange (IDX) for the 2020–2024 period. Using Natural Language Processing (NLP) methods, this study analyzed 50 annual reports in the Management Discussion and Analysis (MD&A) section. The analysis stages included text pre-processing, tokenization, sentiment analysis, and topic modeling using the Latent Dirichlet Allocation (LDA) approach. The results show that financial report narratives in Indonesia tend to have a highly optimistic tone, although this does not always align with actual financial performance. The topic analysis revealed three main narrative patterns: (1) growth and strategy narratives, (2) risk and mitigation narratives, and (3) social responsibility narratives. This study provides a theoretical contribution to the development of a computational linguistics approach in accounting and offers practical implications for auditors, analysts, and regulators in assessing the transparency of financial information.

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