本文主要针对酒店领域的评论信息进行情感分析, 研究用户对于酒店配置、服务等方面的态度, 以便为酒店提高个性化服务质量提供一定的帮助. 本文基于BiLSTM神经网络构建预训练模型进行实验, 同时与传统的机器学习算法进行比较, 实验结果显示, 相较于朴素贝叶斯, 支持向量机的分析准确率更为稳定, 而利用预训练模型进行预测的精确率相比前两者有小幅度的提高; 同时以基础词典为主体, 构建适用于酒店评论的扩展情感词典, 对否定词的权重进行了弱化处理, 减小对带有相反含义语句的分类效果的影响, 将基础词典与扩展词典对获取的同一语料进行情感分类, 比较二者的结果表明采用扩展词典进行正向分类的准确率为86%, 负向分类的准确率为84%, 结果显示扩展词典分类比基础词典的分类效果更好.
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.