Applying academic theory with text mining to offer business insight: Illustration of evaluating hotel service quality

Choong C. Lee, Kun Kim, Haejung Yun

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Now is the time for IS scholars to demonstrate the added value of academic theory through its integration with text mining, clearly outline how to implement this for text mining experts outside of the academic field, and move towards establishing this integration as a standard practice. Therefore, in this study we develop a systematic theory-based text-mining framework (TTMF), and illustrate the use and benefits of TTMF by conducting a text-mining project in an actual business case evaluating and improving hotel service quality using a large volume of actual user-generated reviews. A total of 61,304 sentences extracted from actual customer reviews were successfully allocated to SERVQUAL dimensions, and the pragmatic validity of our model was tested by the OLS regression analysis results between the sentiment scores of each SERVQUAL dimension and customer satisfaction (star rates), and showed significant relationships. As a post-hoc analysis, the results of the co-occurrence analysis to define the root causes of positive and negative service quality perceptions and provide action plans to implement improvements were reported.

Original languageEnglish
Pages (from-to)615-643
Number of pages29
JournalAsia Pacific Journal of Information Systems
Issue number4
StatePublished - 1 Dec 2019

Bibliographical note

Funding Information:
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2016S1A5 A2A03927883).

Publisher Copyright:
© 2019 Korean Society of Management Information Systems.


  • Sentiment Analysis
  • Text Classification
  • Text Mining
  • Theory-based Text Mining Framework (TTMF)


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