Review Article
Social network search based on semantic analysis and learning

https://doi.org/10.1016/j.trit.2016.12.001Get rights and content
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Abstract

Because of everyone's involvement in social networks, social networks are full of massive multimedia data, and events are got released and disseminated through social networks in the form of multi-modal and multi-attribute heterogeneous data. There have been numerous researches on social network search. Considering the spatio-temporal feature of messages and social relationships among users, we summarized an overall social network search framework from the perspective of semantics based on existing researches. For social network search, the acquisition and representation of spatio-temporal data is the basis, the semantic analysis and modeling of social network cross-media big data is an important component, deep semantic learning of social networks is the key research field, and the indexing and ranking mechanism is the indispensable part. This paper reviews the current studies in these fields, and then main challenges of social network search are given. Finally, we give an outlook to the prospect and further work of social network search.

Keywords

Semantic analysis
Semantic learning
Cross-modal
Social network search

Cited by (0)

Feifei Kou was born in 1989. She received her M.S. degree in Computer technology from Beijing Technology and Business University. She is now a Ph.D. candidate in Computer Science and Technology of Beijing University of Posts and Telecommunications. Her research interests include social network search, semantic analysis and semantic learning.

Junping Du was born in 1963. She is now a professor and Ph.D. tutor at the School of Computer Science and Technology, Beijing University of Posts and Telecommunications. Her research interests include artificial intelligence, image processing and pattern recognition.

Yijiang He was born in 1994. He received the B.S. degree in Network Engineering from Nangjing University of Posts and Telecommunications. He is now studying for a master's degree in Computer science and Technology from Beijing University of Posts and Telecommunications. His research interests include data mining and deep learning.

Lingfei Ye was born in Hubei, China. She received the B.S. degree in Network Engineering from Beijing University of Posts and Telecommunications in 2015. She is currently pursuing the Master's degree in Computer Science and Technology from Beijing University of Posts and Telecommunications. Her current research interest is personalized tourism search.

Peer review under responsibility of Chongqing University of Technology.