Coronavirus disease 2019 (COVID-19) poses massive challenges for the world. Public sentiment analysis during the outbreak provides insightful information in making appropriate public health responses. On Sina Weibo a , a popular Chinese social media, posts with negative sentiment are valuable in analyzing public concerns. 999,978 randomly selected COVID-19 related Weibo posts from 1 January 2020 to 18 February 2020 are analyzed. Specifically, the unsupervised BERT (Bidirectional Encoder Representations from Transformers) model is adopted to classify sentiment categories (positive, neutral, and negative) and TF-IDF (term frequency-inverse document frequency) model is used to summarize the topics of posts. Trend analysis and thematic analysis are conducted to identify characteristics of negative sentiment. In general, the fine-tuned BERT conducts sentiment classification with considerable accuracy. Besides, topics extracted by TF-IDF precisely convey characteristics of posts regarding COVID-19. As a result, we observed that people concern four aspects regarding COVID

No abstract

No abstract

Abstract. Acupuncture-point is a very important part in traditional Chinese medicine, but to locate the points is very difficult for non professionals. In this paper, we put forward a new method to locate the acupuncture points of human facial based on the research of the pattern recognition technology in the facial acupuncture points. The method is easy to operate and can be used without professional training, so it can greatly reduce the difficulty of positioning, improve the positioning accuracy, and is beneficial to the application of acupuncture points in the treatment and health care. Experiments show that the method proposed in this paper is effective.

No abstract