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Online customer reviews have become important for managers and executives inthe hospitality and catering industry who wish to obtain a comprehensiveunderstanding of their customers' demands and expectations. We propose aRegularized Text Logistic (RTL) regression model to perform text analytics andsentiment classification on unstructured text data, which automaticallyidentifies a set of statistically significant and operationally insightful wordfeatures, and achieves satisfactory predictive classification accuracy. Weapply the RTL model to two online review datasets, Restaurant and Hotel, fromTripAdvisor. Our results demonstrate satisfactory classification performancecompared with alternative classifiers with a highest true positive rate of94.9%. Moreover, RTL identifies a small set of word features, corresponding to3% for Restaurant and 20% for Hotel, which boosts working efficiency byallowing managers to drill down into a much smaller set of important customerreviews. We also develop the consistency, sparsity and oracle property of theestimator.

Regularised Text Logistic Regression: Key Word Detection and Sentiment Classification for Online Reviews