Abstract
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 Topic Modelling in Text Mining is; (ii) how to Collect Textual Data on Social Media; (iii) what latent Dirichlet Allocation (LDA) and hierarchical latent Dirichlet Allocation (hLDA) are; (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 the limitations of topic modelling are?
| Original language | English |
|---|---|
| Title of host publication | Researching and Analysing Business |
| Subtitle of host publication | Research Methods in Practice |
| Editors | Pantea Foroudi, Charles Dennis |
| Place of Publication | London |
| Publisher | Routledge |
| Chapter | 11 |
| Number of pages | 15 |
| Edition | 1st |
| ISBN (Electronic) | 9781003107774 |
| ISBN (Print) | 9780367620646, 9780367620653 |
| DOIs | |
| Publication status | Published - 14 Dec 2023 |