Using Big Data and Text Analytics to Understand How Customer Experiences Posted on Yelp.com Impact the Hospitality Industry
| Autor: | Wen-Chang Fang, Szu-Ling Chen, Pei-Ju Lucy Ting, Hsiang Chen |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Marketing
Economics and Econometrics biology business.industry Strategy and Management 05 social sciences Big data Management Monitoring Policy and Law biology.organism_classification Hospitality industry Data science Management Information Systems Chen Text mining Analytics Hospitality Management of Technology and Innovation 0502 economics and business Attitudinal analytics 050211 marketing Sociology business Customer intelligence 050203 business & management |
| Zdroj: | Contemporary Management Research. 13:107-130 |
| ISSN: | 1813-5498 |
| DOI: | 10.7903/cmr.17730 |
| Popis: | This study combines programming and data mining to analyze consumer reviews extracted from Yelp.com to deconstruct the hotel guest experience and examine its association with satisfaction ratings. The findings show many important factors in customer reviews that carry varying weights and find the meaningful semantic compositions inside the customer reviews. More importantly, our approach makes it possible to use big data analytics to find different perspectives on variables that might not have been studied in the hospitality literature. Keywords: Big Data, Text Analytics, Data Mining, Social Website, Guest Experience Satisfaction, Hotel Management To cite this document: Pei-Ju Lucy Ting, Szu-Ling Chen, Hsiang Chen, and Wen-Chang Fang, "Using Big Data and Text Analytics to Understand How Customer Experiences Posted on Yelp.com Impact the Hospitality Industry", Contemporary Management Research, Vol.13, No.2, pp. 107-130, 2017. Permanent link to this document: http://dx.doi.org/10.7903/cmr.17730 |
| Databáze: | OpenAIRE |
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