Analyzing Food Community with Recipes and Weibo User Reviews
Wu Xiaolan1,2,Zhang Chengzhi2,3()
1School of Management Science and Engineering, Anhui University of Finance and Economics, Bengbu 233030, China 2Department of Information Management, Nanjing University of Science and Technology, Nanjing 210094, China 3Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210093, China
[Objective] This study examines the structure of online food community with the help of large-scale real world data. [Methods] First, we collected recipes from meishij.net (a popular food network online) and user reviews from Sina Weibo (micro-blog) respectively. Second, we identified the Weibo users who mentioned recipes from meishij.net and mapped them to provinces and cuisines coordinate systems. Finally, we used community discovery algorithm to analyze the food community’s structure. [Results] The province and cuisines networks showed clear community structures. [Limitations] Demographic disparity might pose some effects to the conclusions. [Conclusions] The tastes of consumers from different provinces could be classified as “freshly salty”, “hot and spicy”, as well as “others”. “Sichuan” or “Yungui” dishes are rarely ordered together, while “Jing”, “Hu”, “Lu” and “Dongbei” dishes are often ordered along with each other. Besides, the regional cuisines have some geographical proximity among themselves.
吴小兰,章成志. 基于菜谱与微博用户评论的饮食社区挖掘研究*[J]. 现代图书情报技术, 2016, 32(6): 54-62.
Wu Xiaolan,Zhang Chengzhi. Analyzing Food Community with Recipes and Weibo User Reviews. New Technology of Library and Information Service, 2016, 32(6): 54-62.
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