Text mining analysis roadmap (TMAR) for service research. Issue 1 (8th January 2020)

• Record Type: Journal Article
• Authors: Zaki, Mohamed; McColl-Kennedy, Janet R.
• Is Part Of: Journal of services marketing. Volume 34:Issue 1(2020)
• Page Start: 30
• Page End: 47
• Publication Date: 2020-01-08
• Subjects: Text mining -- Service research -- Machine learning -- Natural language processing -- Qualitative research -- Artificial intelligence; Service industries -- Periodicals; Customer services -- Periodicals; 658.802
• DOI: 10.1108/JSM-02-2019-0074
• Languages: English
• ISSNs: 0887-6045

Abstract

Abstract : Purpose: The purpose of this paper is to offer a step-by-step text mining analysis roadmap (TMAR) for service researchers. The paper provides guidance on how to choose between alternative tools, using illustrative examples from a range of business contexts. Design/methodology/approach: The authors provide a six-stage TMAR on how to use text mining methods in practice. At each stage, the authors provide a guiding question, articulate the aim, identify a range of methods and demonstrate how machine learning and linguistic techniques can be used in practice with illustrative examples drawn from business, from an array of data types, services and contexts. Findings: At each of the six stages, this paper demonstrates useful insights that result from the text mining techniques to provide an in-depth understanding of the phenomenon and actionable insights for research and practice. Originality/value: There is little research to guide scholars and practitioners on how to gain insights from the extensive "big data" that arises from the different data sources. In a first, this paper addresses this important gap highlighting the advantages of using text mining to gain useful insights for theory testing and practice in different service contexts.