Text Mining and Topic Modelling

Yulei Li, Shan Shan, Zhibin Lin

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

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 languageEnglish
Title of host publicationResearching and Analysing Business
Subtitle of host publicationResearch Methods in Practice
EditorsPantea Foroudi, Charles Dennis
Place of PublicationLondon
PublisherRoutledge
Chapter11
Number of pages15
Edition1st
ISBN (Electronic)9781003107774
ISBN (Print)9780367620646, 9780367620653
DOIs
Publication statusPublished - 14 Dec 2023

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