Yang Zaoli, Gao Yue, Fu Xiangling
College of Economics and Management, Beijing University of Technology, Beijing, 100124 China.
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876 China.
Ann Oper Res. 2021 Nov 1:1-17. doi: 10.1007/s10479-021-04339-y.
In the process of hotel reservation on online traveling platforms, online reviews, as a fundamental source where the actual information of a product can be had access to, have been attached with high importance by customers when they have difficulty making a decision on which hotel to pick. However, with enormous amount of online reviews distributed in diverse online traveling platforms, customers tend to have few patience or time to manually read all these reviews and get the exact information they want. Inspired by the widespread application of aspect-based sentiment analysis in the field of data mining, a bidirectional long short-term memory (Bi-LSTM) and attention mechanism based model to predict multiple attributes of a product from online review texts is proposed. Experimental result shows that such Bi-LSTM with attention mechanism model apparently improves the accuracy of the prediction, compared with single LSTM model. Meanwhile, based on the output of the prediction, we analyze and transfer it into a statistical matrix. With an intuitionistic fuzzy compromise decision-making method VIKOR applied, an overall ranking, according to multiple product attributes can be made, in which way to help customers make decisions. To prove the rationality of the algorithm, online hotel reviews from three stream online travelling platforms are crawled as a case.
在在线旅游平台进行酒店预订的过程中，在线评论作为获取产品实际信息的重要来源，在顾客难以决定选择哪家酒店时受到了高度重视。然而，由于大量的在线评论分布在各种在线旅游平台上，顾客往往没有耐心或时间手动阅读所有这些评论并获取他们想要的准确信息。受基于方面的情感分析在数据挖掘领域广泛应用的启发，提出了一种基于双向长短期记忆（Bi-LSTM）和注意力机制的模型，用于从在线评论文本中预测产品的多个属性。实验结果表明，与单LSTM模型相比，这种带有注意力机制的Bi-LSTM模型显著提高了预测的准确性。同时，基于预测输出，我们对其进行分析并转化为一个统计矩阵。应用直觉模糊折衷决策方法VIKOR，可以根据多个产品属性进行总体排名，从而帮助顾客做出决策。为了证明该算法的合理性，抓取了三个主流在线旅游平台的在线酒店评论作为案例。
