Predicting Chinese tourists’ B&B preferences through a method of online reviews causality analytic

Abstract

With the enormous numbers of online reviews posted daily, automatically acquired and analyzed the information that rich of causal logics is of great significance for Chinese B&B enterprises to make accurate business decisions. This study proposed a method based on Chinese NLP technologies to reveal and describe the formation process of tourists' behavioral preferences better than the previous entity relationship extraction and analysis methods. Firstly, Cause-effect syntactic and template pattern had been applied to extraction event casual relations in the online reviews texts. Then, Doc2vec-Kmeans clustering algorithm and key-phrases sorting algorithm were used to generalize data. Finally, causality graph was constructed for visual analysis. To evaluate the performance of the proposed approach, we collected 549, 732 Chinese B&B reviews from Ctrip.com and Qunaer.com. The survey identified 7 common preferences that travelers' concern about nowadays and reveals the reasons behind each of them, as well as concluded that the ‘services’ and ‘conveniences’ of Chinese B&Bs are the trends of travelers’ preferences in the future selections. The results indicated that the proposed approach outperforms the other entity relation extraction method in event forecasting, and most of the subjects believed that the proposed approach can provide more comprehensive and logical traveler's potential demand information.