{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T23:12:39Z","timestamp":1723158759639},"reference-count":26,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Soft. Eng. Knowl. Eng."],"published-print":{"date-parts":[[2021,4]]},"abstract":" Urban region functions are the types of potential activities in an urban region, such as residence, commerce, transportation, entertainment, etc. A service which mines urban region functions is of great value for various applications, including urban planning and transportation management, etc. Many studies have been carried out to dig out different regions\u2019 functions, but few studies are based on social media text analysis. Considering that the semantic information embedded in social media texts is very useful to infer an urban region\u2019s main functions, we design a service which extracts human activities using Sina Weibo ( www.weibo.com ; the largest microblog system in Chinese, similar to Twitter) with location information and further describes a region\u2019s main functions with a function vector based on the human activities. First, we predefine a variety of human activities to get the related activities corresponding to each Weibo post using an urban function classification model. Second, urban regions\u2019 function vectors are generated, with which we can easily do some high-level work such as similar place recommendation. At last, with the function vectors generated, we develop a Web application for urban region function querying. We also conduct a case study among the urban regions in Beijing, and the experiment results demonstrate the feasibility of our method. <\/jats:p>","DOI":"10.1142\/s0218194021400088","type":"journal-article","created":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T07:43:00Z","timestamp":1621323780000},"page":"563-586","source":"Crossref","is-referenced-by-count":4,"title":["Urban Region Function Mining Service Based on Social Media Text Analysis"],"prefix":"10.1142","volume":"31","author":[{"given":"Yanchun","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Peking University, Beijing 100871, P. R. China"},{"name":"Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing 100871, P. R. China"},{"name":"Peking University Information Technology Institute (Tianjin Binhai), Tianjin 300450, P. R. China"}]},{"given":"Hang","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Peking University, Beijing 100871, P. R. China"},{"name":"Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing 100871, P. R. China"},{"name":"Peking University Information Technology Institute (Tianjin Binhai), Tianjin 300450, P. R. China"}]},{"given":"Jiu","family":"Wen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Peking University, Beijing 100871, P. R. China"},{"name":"Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing 100871, P. R. China"},{"name":"Peking University Information Technology Institute (Tianjin Binhai), Tianjin 300450, P. R. China"}]},{"given":"Zhiyu","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Peking University, Beijing 100871, P. R. 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