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Text Mining and Topic Modelling

Li, Yulei; Shan, Shan; Lin, Zhibin


Yulei Li
PGR Student Doctor of Philosophy

Shan Shan


Pantea Foroudi

Charles Dennis


Social media platforms have become a prevalent place where customers can share their real opinions about products, services, or brands. This encourages businesses to invest abounding resources to analyse and understand what their customers are discussing on social media. This chapter will attempt to introduce one application of natural language processing (NLP) or text mining in business research. This chapter focuses on understanding (i) what is the Topic Modelling in Text Mining?, (ii) how to Collect Textual Data on Social Media?, (iii) what are latent Dirichlet Allocation (LDA) and hierarchical latent Dirichlet Allocation (hLDA)?, (iv) how to visualise the hierarchical topics generated by hLDA?, (v) how to interpret the hLDA results?, (vi) how to write the results or findings section for hLDA results?, and (vii) what are the limitations of topic modelling?


Li, Y., Shan, S., & Lin, Z. (2023). Text Mining and Topic Modelling. In P. Foroudi, & C. Dennis (Eds.), Researching and Analysing Business: Research Methods in Practice. London: Routledge.

Online Publication Date Dec 14, 2023
Publication Date Dec 14, 2023
Deposit Date Dec 8, 2023
Publicly Available Date Jun 15, 2025
Publisher Routledge
Edition 1st Edition
Book Title Researching and Analysing Business: Research Methods in Practice
Chapter Number 11
Public URL