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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.

A decision-making algorithm combining the aspect-based sentiment analysis and intuitionistic fuzzy-VIKOR for online hotel reservation