TY - JOUR
T1 - Insights into TripAdvisor's online reviews
T2 - The case of Tehran's hotels
AU - Khorsand, Ramina
AU - Rafiee, Majid
AU - Kayvanfar, Vahid
N1 - Publisher Copyright:
© 2020
PY - 2020/4/3
Y1 - 2020/4/3
N2 - User-generated data in TripAdvisor.com consists of considerable amount of useful information that can help managers to provide better services to their customers. This study aims to forecast a new user's rate to a hotel based on information of the hotel and user. To do so, all reviews on all hotels of Tehran on TripAdvisor.com as real data are selected and 8 different supervised machine learning models are applied to the data to select the best method including K-nearest neighbors (KNN), Naïve Bayes, decision tree, logistic regression, support vector machine, neural network, random forest, and gradient boosting. KNN algorithm which uses similarity and distance measures for classification is selected as the best method through conducted comprehensive comparisons, statistical analysis and data-based sensitivity analysis. Since this study investigates an intensive set of data of all hotels in a city in all time, some worthful managerial insights are presented.
AB - User-generated data in TripAdvisor.com consists of considerable amount of useful information that can help managers to provide better services to their customers. This study aims to forecast a new user's rate to a hotel based on information of the hotel and user. To do so, all reviews on all hotels of Tehran on TripAdvisor.com as real data are selected and 8 different supervised machine learning models are applied to the data to select the best method including K-nearest neighbors (KNN), Naïve Bayes, decision tree, logistic regression, support vector machine, neural network, random forest, and gradient boosting. KNN algorithm which uses similarity and distance measures for classification is selected as the best method through conducted comprehensive comparisons, statistical analysis and data-based sensitivity analysis. Since this study investigates an intensive set of data of all hotels in a city in all time, some worthful managerial insights are presented.
KW - Customer rating
KW - Data mining
KW - Hospitality
KW - Machine learning
KW - Online hotel reviews
UR - https://www.scopus.com/pages/publications/85082770164
U2 - 10.1016/j.tmp.2020.100673
DO - 10.1016/j.tmp.2020.100673
M3 - Article
AN - SCOPUS:85082770164
SN - 2211-9736
VL - 34
JO - Tourism Management Perspectives
JF - Tourism Management Perspectives
M1 - 100673
ER -