Regularised Text Logistic Regression: Key Word Detection and Sentiment Classification for Online Reviews
This provides hospitality managers with an efficient tool for analyzing customer feedback by focusing on a small subset of important word features (3-20% of total).
The authors tackled sentiment classification of online reviews by proposing a Regularized Text Logistic (RTL) regression model that identifies key word features, achieving up to 94.9% true positive rate on TripAdvisor datasets.
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.