In order to analyze the sentimental focus of the comment data from users of masks during the outbreak of virus, we extracted 143 330 comments about the purchase from Taobao users from March 1st to April 11th, 2020 by means of the Web Scraper of Google browser. To improve the accuracy of the sentimental estimation, each comment of the total 14 400 pieces was manually marked as positive or negative emotion on this data set. And then we used SnowNLP, the sentimental analysis model to train them. At last, the trained corpus was used for sentimental estimation. The overall sentiment of the comments was proved positive. On the basis of the daily emotional variation trend of users’ comments, the trend of local new cases (excluding overseas input) to some extent affects the overall change of their daily emotional trend. And the local fluctuation trend of domestic new cases (including overseas input) also affects that of the everyday emotional performance. After classifying the predicted comments, we found that users’ positive comments focused on the quality, packaging, price, and thickness of masks, while negative comments focused on the quality, packaging, smell, and whether the masks were for medical use.