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Text Retrieval and Data Mining in SI - An Introduction

Text Retrieval and Data Mining in SI - An Introduction. Qiaozhu Mei School of Information Computer Science and Engineering University of Michigan qmei@umich.edu. Challenge of Data Mining. Published Content: 3-4G/day User generated data: 8-10G/day Private text data: 3T/day.

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Text Retrieval and Data Mining in SI - An Introduction

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  1. Text Retrieval and Data Mining in SI - An Introduction Qiaozhu Mei School of Information Computer Science and Engineering University of Michigan qmei@umich.edu

  2. Challenge of Data Mining Published Content: 3-4G/day User generated data: 8-10G/day Private text data: 3T/day - Ramakrishnan and Tomkins 2007

  3. What do We Do in this Battle? Social Network Mining Social networks Query logs Crowd Online communities Social bookmarks Academic networks Social Data Mining Information networks Social media Health Informatics User tweets Web Search sentiments Content blogs authorship Context impact Scientific Literature Information Retrieval Contextual Text Mining location time News articles EHR Statistical Topic Modeling event Web pages Topics Bioinformatics

  4. Personalization v.s. Diversification MSR  Mountain Safety Research + Microsoft Research + Metropolis Street Racer … Mountain Safety Research + MSR Tents + MSR Wheels + Microsoft Research … Microsoft Research + Microsoft Research Redmond + Microsoft Research Asia … ? ? ? Diverse Rank PageRank Personalized Rank - Joint work with Jian Guo, Qian Zhen

  5. Topic Evolution and Trends Hot Topics in SIGMOD What’s hot in literature/twitter?

  6. Modeling Spatiotemporal Topic Diffusion One Week Later How does discussion spread? “government response in hurricane Katrina” Topic =

  7. Summarizing and Tracking Opinions The Da Vinci Code Blogs; customer reviews Tom Hanks, who is my favorite movie star act the leading role. protesting... will lose your faith by watching the movie. ... so sick of people making such a big deal about a fiction book a good book to past time. What is good and what is bad?

  8. Topical Community Detection Social/Academic Network Information retrieval community Data mining community Machine learning community Who works together on what? Text Content

  9. Thanks! • Joint work with Cheng Zhai, Ken Church, Bruce Schatz, Ravi Kumar, Andrew Tomkins, Denny Zhou, Jian Guo, Qian Zhen, Xu Ling, Duo Zhang, Deng Cai, Dong Xin, Chao Liu ...

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