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PurposeTo examine the relationship between online comments, merchant replies and online sales of tourism products and focus on the moderating role of tourist destination.Design/methodology/approachThis article uses crawler technology and regression analysis methods.FindingsThe researchers found the following: (1) The number of pictures uploaded with online comments, the number of merchant replies and the length of merchant replies have a significant positive effect on sales of tourism products, while the length of comments and the similarity of merchant replies negatively affect sales of tourism products. The emotional scores of the reviews do not significantly affect sales of tourism products. (2) Tourist destination moderates the relationship between user comments and sales of tourism products. The length of comments has a greater negative effect on sales of domestic tourism products, while the number of comments has a greater positive effect on sales of overseas tourism products. (3) Tourist destination moderates the relationship between merchant replies and sales of tourism products. Consumers who choose domestic tourism products pay more attention to the interactivity reflected by merchant replies (e.g. number and length of merchant replies), while consumers who choose overseas tourism products hope to receive replies that are more useful, such as reply similarity.Originality/valueThe research conclusions enrich the relevant research in the field of online review research and has practical significance for how companies increase sales of tourism products.

Identifying and analyzing the public’s opinion of focal events during a major epidemic can help the government grasp the vicissitudes of network public opinion in a timely manner and provide the appropriate responses. Taking the COVID-19 epidemic as an example, this study begins by using Python-selenium to capture the original text and comment data related to COVID-19 from Sina Microblog’s CCTV News from Jan. 19, 2020, to Feb. 20, 2020. The study subsequently uses a manual interpretation method to classify the Weibo content and analyzes the shifting focus phenomena of network public opinion based on the moving average method. Next, the study uses an enhances TF-IDF to extract keywords from the Weibo comment and uses the keywords to construct a word co-occurrence network. The results show that during the epidemic, the network public opinion focus shifted significantly over time. With the progression of the epidemic, the focus of network public opinion diversified, and various categories stabilized. Compared to simple keyword and text classification recognition focus problems, the proposed model, which is highly accurate, identified multiple network public opinion focus problems and described the core contradictions of the different focus problems.