Training attractive attribute classifiers based on opinion features extracted from review data

Abstract Researchers have proposed statistical regression models that analyse on-line review data to identify attractive attributes of a product or service. This research has the same aim, but with an approach based on machine learning models instead of statistical models. The proposed approach first extracts attribute-level sentiments from the review text by natural language processing techniques, then derives features that reflect the non-linear relations between attribute performance and customer satisfaction based on the sentiments. The non-linear features are fed to the Support Vector Machine (SVM) model to train predictive attractive attribute classifiers. The proposed approach is evaluated on a hotel review dataset crawled from TripAdvisor. The experiment results indicate that the classifiers reach a precision of 79.3% and outperform the existing statistical models by a margin of over 10%.

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