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New Technology of Library and Information Service  2016, Vol. 32 Issue (10): 70-80    DOI: 10.11925/infotech.1003-3513.2016.10.08
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Identifying Food Topics from User-Generated Contents in Microblogs
Zhang Xiaoyong1,2,Zhou Qingqing1,2,Zhang Chengzhi1,2,3()
1School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China
2 Alibaba Research Center for Complex Sciences, Hangzhou Normal University, Hangzhou 311121, China
3 Jiangsu Key Laboratory of Data Engineering and Knowledge Service (Nanjing University), Nanjing 210093, China
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[Objective] This study aims to identify microblog post topics, and then automatically extract high quality ones with the help of text clustering techniques. [Methods] We collected food related microblog posts from Sina Weibo as raw data, then applied text clustering and deep learning techniques to detect the target topics. First, we categorized the microblog posts by the four seasons in accordance with their publishing dates. Second, we created a vector space model and used text clustering method to retrieve candidate topics. Finally, we automatically identified the quality topics with deep learning technology. [Results] We automatically identified the high quality topics manually found by researchers, and their topic coverage values were all higher than 0.5. [Limitations] We decided the topic quality based on qualitative data. [Conclusions] The proposed method could extract high quality topics effectively. The retrieved topics reflect the distribution of food related microblog posts in the four seasons.

Key wordsTopic detection      User-Generated Contents      Topic coverage      Food mining     
Received: 26 May 2016      Published: 23 November 2016

Cite this article:

Zhang Xiaoyong,Zhou Qingqing,Zhang Chengzhi. Identifying Food Topics from User-Generated Contents in Microblogs. New Technology of Library and Information Service, 2016, 32(10): 70-80.

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