Unfolding social content evolution along time and semantics
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- 作者：Carmen De Maioa ; cdemaio@unisa.it" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Giuseppe Fenzab ; gfenza@unisa.it" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Vincenzo Loiab ; loia@unisa.it" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Francesco Orciuolib ; forciuoli@unisa.it" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae
- 关键词：Temporal Concept Analysis ; Social media ; Data mining ; Fuzzy Formal Concept Analysis ; Description Logic
- 刊名：Future Generation Computer Systems
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：66
- 期：Complete
- 页码：146-159
- 全文大小：2315 K
文摘
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The defined approach faces with a generalized version of TDT problem to track any kind of entity or scope along time and semantic dimensions.
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The proposed framework relies on Temporal Fuzzy Concept Analysis that enable us to analyze at the same stage both time relations among tweets and their semantic meaning of their content.
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Description Logic enable us to decouple the tweet analysis and understanding from the definition of the tracking scope (user’s query processing) enabling the system to support different kind of analytics perspectives at run time.