A Comparative Study of Multi-Class Sentiment Classification Models for Hotel Customer Reviews

This research intends to provide a comparative analysis of multi-class sentiment classification models for hotel customer reviews. This study utilized a dataset of hotel reviews downloaded from the TripAdvisor website. These hotel reviews were composed in English and were based on a five-star rating scale. The rating of each hotel reflected its class. There was a comparison between transformer-based sentiment classification algorithms (such as BERT) and traditional sentiment classification algorithms. The traditional algorithms for sentiment classification in this study can be machine learning (e.g. Multinomial Naïve Bayes, Random Forest, and Support Vector Machines with linear kernel function) and deep learning (e.g. Convolutional Neural Networks). After evaluation via recall, precision, F1, accuracy, and AUC, the BERT model outperformed other models such as Multinomial Nave Bayes, Random Forest, and Support Vector Machines, and Convolutional Neural Networks.