Qingxia Liu qxliu.nju@gmail.com Interactive Hierarchical Tag Clouds for Summarizing Spatiotemporal Social Contents [ICDE 2014] Kang, Wei, Anthony KH Tung, Feng Zhao, and Xinyu Li Qingxia Liu qxliu.nju@gmail.com
Websoft Research Group Introduction Goal interactive exploration of regions by summarizing and browsing social network contents Input Social network contents: textual microblogs e.g. tweets User specified time, region Output Topic hierarchies Websoft Research Group
Vesta: http://db128gb-b.ddns.comp.nus.edu.sg/kangwei/bicluster/
Partition-and-merge Scheme in Vesta biclustering time latitude longitude hLDA Websoft Research Group
Websoft Research Group Biclustering Bicluster (T, C) Cluster in two directions (tags, contents) Density: non-zero rate of corresponding submatrix Formal context (A, O, I) -- bicluster Fullness: 所对应的矩阵全为1 Maximum: 再添如任一行或列都将引入0值 Galois operations A’, A’’ Biclustering 双聚类、联合聚类 Formal Concept Analysis （FCA） 形式概念分析 Fullness: 任意a，o属于A,O, (a,o)∈I，即任意一对a,o均存在关系I Maximum：新加入任何a或者o都会引入不存在关系I的a,o对 Websoft Research Group
Websoft Research Group Online Merging Merging Biclusters sharing most common tags δden = # of 1s/ # of total entries Websoft Research Group
Online Merging Merging Topic Hierarchies Given: m topic hierarchies, # of levels n0, # of tags for each level of the result Each tag only appears in one level Weight function
Evaluation Performance
Evaluation Precision & Recall Offline scalability
Websoft Research Group Conclusion Contributions Summary generation by biclustering Extended disk-based PM scheme Topic hierarchies by hLDA and merging Websoft Research Group
Websoft Research Group v.s. RDF data management Similarities Collection of information containers tweets v.s. entity descriptions Can be clustered into topics Differences Independent plain texts v.s. Linked entities Feature : keywords v.s. property,value Future Work Entity Feature extraction Topic generation Link summarization Websoft Research Group
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