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Twitterpedia

Twitterpedia. Visualization Lab By: Thomas Kraft. Overview. Current State. Future. Problem. What is being talked about and where? Twitter has massive amounts of data Tweets are unstructured Goal: Quickly identify current events / topics on a large scale. Overview. Current State.

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Twitterpedia

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  1. Twitterpedia Visualization Lab By: Thomas Kraft

  2. Overview Current State Future Problem • What is being talked about and where? • Twitter has massive amounts of data • Tweets are unstructured • Goal: Quickly identify current events / topics on a large scale

  3. Overview Current State Future What Needs To Be Done • Data Collection • Database • Web Crawler • Analyze Data • Topic Modeling • Get Trends and topics!

  4. Overview Current State Future Hadoop • Processes large datasets • Splits data into chunks • Data processed on multiple machines • Very Scalable • Add/remove computers easily • As dataset grows so can # of machines

  5. Overview Current State Future Computer Cluster

  6. Overview Current State Future Topic Modeling • Latent Dirichlet Allocation (LDA) • Correlations between words in topics • Topics composed of keyword groups • Tweets topic can effectively be inferred

  7. Overview Current State Future June 26, 2011 “Bruno & alicia! I love it!” “Can Rick Ross Please put his clothes on?”

  8. Overview CurrentState Future Challenge • Topic Modeling Resource Intensive • Iterates over data • Single Computer can’t handle large dataset • Solution: Parallelize the process

  9. Overview Current State Future Parallel - LDA • Write algorithm to split up tweets and join output • Improves scalability for LDA • Shows near linear improvements • PLDA will take twitterpedia to next level • Larger datasets with quicker processing

  10. Overview Current State Future Future • Write algorithm to parallelize tweet distribution and aggregation • Create website implementing topics

  11. Overview Current State Future Conclusion • Working on this project has been a great learning experience • Designed and managed a large database • Efficiency high priority • Learned cool tricks along the way…

  12. Overview Current State Future Thanks • A Special thanks to my advisor Xiaoyu Wang, Wenwen Dou, and to the Visualization Center • Thomas Kraft : tbkraf08@stlawu.edu

  13. Questions?

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