Identifying and Analyzing Popular Phrases Multi-Dimensionally in Social Media Data

Zhongying Zhao, Chao Li, Yong Zhang, Joshua Zhexue Huang, Jun Luo, Shengzhong Feng and Jianping Fan
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Zhongying Zhao: College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao, China & Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, China
Chao Li: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Yong Zhang: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Joshua Zhexue Huang: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Jun Luo: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Shengzhong Feng: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Jianping Fan: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

International Journal of Data Warehousing and Mining (IJDWM), 2015, vol. 11, issue 3, 98-112

Abstract: With the success of social media, social network analysis has become a very hot research topic and attracted much attention in the last decade. Most studies focus on analyzing the whole network from the perspective of topology or contents. However, there is still no systematic model proposed for multi-dimensional analysis on big social media data. Furthermore, little work has been done on identifying emerging new popular phrases and analyzing them multi-dimensionally. In this paper, the authors first propose an interactive systematic framework. In order to detect the emerging new popular phrases effectively and efficiently, they present an N-Pat Tree model and give some filtering mechanisms. They also propose an algorithm to find and analyze new popular phrases multi-dimensionally. The experiments on one-year Tencent-Microblogs data have demonstrated the effectiveness of their work and shown many meaningful results.

Date: 2015
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