Hotel Stock Prediction Based on Multimodal Deep Learning
Liu Yang1,2,Zhang Wen1,Hu Yi3,Mao Jin2(),Huang Fei4
1School of Information Management, Wuhan University, Wuhan 430072, China 2Center for Studies of Information Resource, Wuhan University,Wuhan 430072, China 3School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China 4Economics and Management School, Wuhan University, Wuhan 430072, China
[Objective] This paper aims to predict the price trend of hotel stocks by analyzing consumer sentiment in tourism reviews using multi-modal deep learning methods. [Methods] First, we constructed a multi-modal deep learning model to encode the multi-modal information. Then, we extracted the interaction information between texts and images through LSTM and graph neural network. Finally, we predicted the price of hotel stocks. [Results] We conducted an empirical study using Yelp’s tourism review data. Compared with the baseline models, the proposed model has superiority, and the average accuracy of stock prediction reached 59.10%. [Limitations] The proposed model was only tested on the dataset of four hotels on the Yelp website and has not been further validated on other tourism platforms. [Conclusions] The proposed model can effectively extract the interactive information between different modalities and improve the accuracy of hotel stock prediction.
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