Song Cen, Guo Chunyu, Hunt Kyle, Zhuang Jun
School of Economics and Management, China University of Petroleum, Beijing 102249, China.
Department of Industrial and System Engineering, University at Buffalo, Buffalo, NY 14260, USA.
Foods. 2020 Apr 18;9(4):511. doi: 10.3390/foods9040511.
Take-away food (also referred to as "take-out" food in different regions of the world) is a very convenient and popular dining choice for millions of people. In this article, we collect online textual data regarding "take-away food safety" from Sina Weibo between 2015 and 2018 using the Octopus Collector. After the posts from Sina Weibo were preprocessed, users' emotions and opinions were analyzed using natural language processing. To our knowledge, little work has studied public opinions regarding take-away food safety. This paper fills this gap by using latent Dirichlet allocation (LDA) and -means to extract and cluster topics from the posts, allowing for the users' emotions and related opinions to be mined and analyzed. The results of this research are as follows: (1) data analysis showed that the degree of topics have increased over the years, and there are a variety of topics about take-away food safety; (2) emotional analysis showed that 93.8% of the posts were positive; and (3) topic analysis showed that the topic of public discussion is diverse and rich. Our analysis of public opinion on take-away food safety generates insights for government and industry stakeholders to promote the healthy and vigorous development of the food industry.
外卖食品（在世界不同地区也被称为“外带”食品）对数以百万计的人来说是一种非常方便且受欢迎的就餐选择。在本文中，我们使用章鱼采集器收集了2015年至2018年期间来自新浪微博的关于“外卖食品安全”的在线文本数据。对新浪微博的帖子进行预处理后，使用自然语言处理分析用户的情绪和观点。据我们所知，很少有研究探讨公众对外卖食品安全的看法。本文通过使用潜在狄利克雷分配（LDA）和K均值算法从帖子中提取和聚类主题，从而挖掘和分析用户的情绪及相关观点，填补了这一空白。本研究结果如下：（1）数据分析表明，这些年主题的关注度有所增加，并且存在各种关于外卖食品安全的主题；（2）情感分析表明，93.8%的帖子是积极的；（3）主题分析表明，公众讨论的主题多样且丰富。我们对外卖食品安全公众意见的分析为政府和行业利益相关者提供了见解，以促进食品行业的健康蓬勃发展。
