Investigation of Topic Modelling Methods for Understanding the Reports of the Mining Projects in Queensland

TL;DR

This study compares three topic modelling methods to identify the most effective approach for organizing and understanding mining project reports from Queensland, aiding knowledge retrieval and document management.

Contribution

It evaluates LDA, NMF, and NTF on mining reports, providing insights into their effectiveness and guiding future application of topic modelling in this domain.

Findings

LDA performs best among the three methods for this dataset.

NMF and NTF could be improved for better performance.

Topic modelling helps organize unstructured mining reports.

Abstract

In the mining industry, many reports are generated in the project management process. These past documents are a great resource of knowledge for future success. However, it would be a tedious and challenging task to retrieve the necessary information if the documents are unorganized and unstructured. Document clustering is a powerful approach to cope with the problem, and many methods have been introduced in past studies. Nonetheless, there is no silver bullet that can perform the best for any types of documents. Thus, exploratory studies are required to apply the clustering methods for new datasets. In this study, we will investigate multiple topic modelling (TM) methods. The objectives are finding the appropriate approach for the mining project reports using the dataset of the Geological Survey of Queensland, Department of Resources, Queensland Government, and understanding the contents to get the idea of how to organise them. Three TM methods, Latent Dirichlet Allocation (LDA), Nonnegative Matrix Factorization (NMF), and Nonnegative Tensor Factorization (NTF) are compared statistically and qualitatively. After the evaluation, we conclude that the LDA performs the best for the dataset; however, the possibility remains that the other methods could be adopted with some improvements.