Utilizing text-mining to explore consumer happiness within tourism destinations

Benjamin Garner*, Corliss Thornton, Anita Luo Pawluk, Roberto Mora Cortez, Wesley Johnston, Cesar Ayala

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


Under growing pressure to demonstrate its societal value, marketing research has the opportunity to focus more on increasing our understanding of consumer happiness. The present research uses topic modeling to interpret and categorize comments from Yelp.com reviews about travel dimensions. In addition, sentiment analysis was used to capture the number of positive and negative words in each review. The data analysis is used to extract and explore the dominant consumer emotions surrounding travel. This research contributes to the practice of marketing and society more broadly by providing an understanding of how memorable experiences are shaped in the travel context and also by demonstrating how machine learning (text mining) can help better understand concepts relating to consumer happiness and well-being.

Original languageEnglish
JournalJournal of Business Research
Publication statusE-pub ahead of print - 8. Sep 2021


  • Consumer behavior
  • Happiness
  • Sentiment analysis
  • Text mining
  • Travel
  • Well-being


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