Publication Type

Working Paper

Version

submittedVersion

Publication Date

1-2024

Abstract

Topic modeling and LDA (Latent Dirichlet Allocation) have proven valuable in various fields as an innovative approach to studying areas of interest and identifying topics in a dynamic content. The underlying assumption is that techniques like LDA can swiftly capture emerging topics in textual documents compared to other categorization tools. These unsupervised approaches have been used to identify new industries and technological domains. However, our study on the nascent topic of “sustainability” within the corpora of SGX-listed companies highlights clear limitations in employing techniques like LDA on sparse data. The dynamic LDA approach, also called DTM (Dynamic Topic Modelling),based on an 11-year database of annual reports from publicly listed companies in Singapore, could not detect sustainability’s rise as a critical topic in corporate practice following policy changes. Moreover, despite sustainability reporting becoming mandatory, sustainability-related topics may still not receive significant attention.

Keywords

unsupervised learning, Latent Dirichlet Allocation (LDA), sustainability, Singapore Exchange (SGX)

Discipline

Asian Studies | Business Law, Public Responsibility, and Ethics | Finance and Financial Management

Research Areas

Strategy and Organisation

First Page

1

Last Page

43

Publisher

Business Perspectives

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.2139/ssrn.4686328

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