Predicting overall customer satisfaction: Big data evidence from hotel online textual reviews

By Yabing Zhao, Xun Xu, Mingshu Wang. International Journal of Hospitality Management, 2019

469 citations recorded by the source corpus.

Abstract Customer online reviews of hotels have significant business value in the e-commerce and big data era. Online textual reviews have an open-structured form, and the technical side, namely the linguistic attributes of online textual reviews, is still largely under-explored. Using a sample of 127,629 reviews from tripadvisor.com, this study predicts overall customer satisfaction using the technical attributes of online textual reviews and customers’ involvement in the review community. We find that a higher level of subjectivity and readability and a longer length of textual review lead to lower overall customer satisfaction, and a higher level of diversity and sentiment polarity of textual review leads to higher overall customer satisfaction. We also find that customers’ review involvement positively influences their overall satisfaction. We provide implications for hoteliers to better understand customer online review behavior and implement efficient online review management actions to use electronic word of mouth and enhance hotels’ performance.

View Paper: https://www.semanticscholar.org/paper/feb2464786168e0c24d7b261e538b45d5d09aec3

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1. Chaitanya Kulkarni
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Citations in the corpus

• A survey on sentiment analysis methods, applications, and challenges (2022, background): “Sentimental analysis on reviews on hotels and restaurants can help customers choose better and also help the owners improve (Zhao et al. 2019).”