The outbreak and spread of COVID-19 have a great impact on the tourism industry. In this paper, we focus on the cultural tourist attractions, and take the Palace Museum as an example to explore and analyze the sentiment from perspective of tourism management under the influence of the epidemic. Firstly, more than 40,000 online reviews before and during the epidemic are crawled from some well-known domestic tourism e-commerce platforms. Then, the deep learning method based on BiLSTM is used to establish the emotion polarity classifier, and the classifier has an accuracy rate of more than 80% on the test set. Afterwards, K-means algorithm is used for the dimension clustering of the review data, and combined with the tourism management factors, the specific and managerial dimension division is carried out. Finally, suggestions for the current epidemic management plan of the Palace Museum and feasible plans for future development are put forward, which can be used as a reference for other cultural tourist attractions.
Zhou, Kailin; Yao, Zhong; Xu, Wuhuan; and Wang, Jiaqi, "Sentiment Analysis of Tourism Online Reviews Using the Deep Learning Method Based on BiLSTM" (2022). WHICEB 2022 Proceedings. 59.