This study mainly analyzes the sentiment of user reviews on hotels by investigating the attitudes of users toward hotel configuration and service to help hotels improve the quality of personalized service. Specifically, a pretraining model based on the BiLSTM neural network is built and compared with traditional machine learning algorithms. The experimental results reveal that the analysis accuracy of support vector machines (SVMs) is more stable compared with that of naive Bayes, while the prediction accuracy using the pretraining model is slightly improved compared with that of the previous two. Moreover, an extended dictionary of sentiment, with the basic dictionary as the main part, is constructed for reviews on hotels, and the weights of negatives are weakened to reduce the impact on the classification of sentences with opposite meanings. The basic dictionary and the extended dictionary are used to classify the sentiment of the same corpus obtained, and the comparison of the results indicates that with the extended dictionary, the accuracy of the positive classification and negative classification is 86% and 84%, respectively. This indicates that the classification effect of the extended dictionary is better than that of the basic dictionary.