  Online customer reviews have become important for managers and executives in
the hospitality and catering industry who wish to obtain a comprehensive
understanding of their customers' demands and expectations. We propose a
Regularized Text Logistic (RTL) regression model to perform text analytics and
sentiment classification on unstructured text data, which automatically
identifies a set of statistically significant and operationally insightful word
features, and achieves satisfactory predictive classification accuracy. We
apply the RTL model to two online review datasets, Restaurant and Hotel, from
TripAdvisor. Our results demonstrate satisfactory classification performance
compared with alternative classifiers with a highest true positive rate of
94.9%. Moreover, RTL identifies a small set of word features, corresponding to
3% for Restaurant and 20% for Hotel, which boosts working efficiency by
allowing managers to drill down into a much smaller set of important customer
reviews. We also develop the consistency, sparsity and oracle property of the
estimator.
