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Semantic Twitter:Analyzing Tweets for Real-time Event Notification<br />Makoto Okazaki and Yutaka Matsuo<br />The Universi...
Twitter<br />Popular microblogging service<br />Short message within 140 characters<br />Real-time nature<br />
Studies on Twitter<br />Why we twitter: Understanding microblogging usage and communities(Java et al. 2007)<br />Analysis ...
Work on Semantic Web<br />How to integrate linked data on the web<br />Automatic extraction of semantic data<br />Extracti...
Idea<br />Means of integrating semantic processing and the real-time nature of Twitter have not been well studied<br />Com...
Proposal<br />Tweet delivery system<br />Delivering some tweets if they are semantically relevant to users’ information ne...
The concept of system<br />Useful information<br />Un-useful information<br />Mass media<br />Semantic<br />technology<br ...
Earthquake information<br />Lots of earthquakes in Japan.<br />Earthquake information is much more valuable if given in re...
Earthquake information system<br />Our System<br />tweet<br />E-mail<br />shook!<br />Distance  from the earthquake center...
System architecture<br />Twitter search API<br />Queries<br />Tweets<br />“Earthquake”<br />“Shakes”<br />Our system<br />...
Classification<br />Clarifying that tweet is really referring to an actual earthquake occurring<br />Classifier using supp...
Features<br />Group A: simple statistical features<br />The number of words in a tweet, and the position of the query word...
Performance of classification<br /><ul><li>Group A: simple statistical features
the number of words in a tweet, and the position of the query word in a tweet
Group B: keyword features
the words in a tweet
Group C: context word features
he words before and after the query word</li></li></ul><li>System architecture<br />Twitter search API<br />Queries<br />T...
Registration<br />The detection of the past earthquakes<br />
Facts about earthquake detection<br />
The number of tweets on earthquakes<br />
E-mail<br />The location is obtained by a registered location on the user profile on twitter.<br />Dear Alice,<br />We hav...
Another prototype<br />Rainbow information<br />Using a similar approach used for detecting earthquakes.<br />Not so time-...
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Semantic Twitter Analyzing Tweets For Real Time Event Notification

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  • I'm involved in a similar project! Please check out: http://www.semanticprofiling.net is about my progress and discoveries related to my Master Thesis in Computer Science Engineering. It is titled: “Scientific profiling based on semantic analysis in social networks”.
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Transcript of "Semantic Twitter Analyzing Tweets For Real Time Event Notification"

  1. 1. Semantic Twitter:Analyzing Tweets for Real-time Event Notification<br />Makoto Okazaki and Yutaka Matsuo<br />The University of Tokyo<br />
  2. 2. Twitter<br />Popular microblogging service<br />Short message within 140 characters<br />Real-time nature<br />
  3. 3. Studies on Twitter<br />Why we twitter: Understanding microblogging usage and communities(Java et al. 2007)<br />Analysis indicators for communities on microblogging platforms(Grosseck et al. 2009)<br />Microblogging for language learning(Borau et al. 2009)<br />Microblogging: A semantic and distributed approach(Passant et al. 2008)<br />
  4. 4. Work on Semantic Web<br />How to integrate linked data on the web<br />Automatic extraction of semantic data<br />Extracting relation among entities from web pages<br />Extracting events<br />
  5. 5. Idea<br />Means of integrating semantic processing and the real-time nature of Twitter have not been well studied<br />Combining these two directions, we can make various algorithms to process twitter data semantically<br />
  6. 6. Proposal<br />Tweet delivery system<br />Delivering some tweets if they are semantically relevant to users’ information need<br />Example: earthquake, rainbow, traffic jam<br />Earthquake prediction system targeting on Japanese tweets<br />
  7. 7. The concept of system<br />Useful information<br />Un-useful information<br />Mass media<br />Semantic<br />technology<br />Information<br />User<br />Real-timeliness: low<br />Real-timeliness: high<br />Real-timeliness: high<br />Usefulness: high<br />Usefulness: low<br />Usefulness: high<br />Mass media<br />Advanced social medium<br />Social media<br />
  8. 8. Earthquake information<br />Lots of earthquakes in Japan.<br />Earthquake information is much more valuable if given in real time.<br />Japanese government has allocated a considerable amount of its budget.<br />Gathering information about earthquakes from twitter.<br />
  9. 9. Earthquake information system<br />Our System<br />tweet<br />E-mail<br />shook!<br />Distance from the earthquake center<br />Earthquake center<br />
  10. 10. System architecture<br />Twitter search API<br />Queries<br />Tweets<br />“Earthquake”<br />“Shakes”<br />Our system<br />Fetcher<br />Text Analyzer<br />DB<br />Mecab<br />SVM<br />Detect tweets about the target event<br />Sender<br />E-mail<br />User<br />User<br />…<br />User<br />User<br />
  11. 11. Classification<br />Clarifying that tweet is really referring to an actual earthquake occurring<br />Classifier using support vector machine(SVM)<br />Preparing 597 examples as a training set<br />
  12. 12. Features<br />Group A: simple statistical features<br />The number of words in a tweet, and the position of the query word in a tweet<br />Group B: keyword features<br />The words in a tweet.<br />The number of each words in a tweet.<br />Group C: context word features<br />The words before and after the query word<br />
  13. 13. Performance of classification<br /><ul><li>Group A: simple statistical features
  14. 14. the number of words in a tweet, and the position of the query word in a tweet
  15. 15. Group B: keyword features
  16. 16. the words in a tweet
  17. 17. Group C: context word features
  18. 18. he words before and after the query word</li></li></ul><li>System architecture<br />Twitter search API<br />Queries<br />Tweets<br />“Earthquake”<br />“Shakes”<br />Our system<br />Fetcher<br />Text Analyzer<br />DB<br />Mecab<br />SVM<br />Detect tweets about the target event<br />Sender<br />E-mail<br />User<br />User<br />…<br />User<br />User<br />
  19. 19. Registration<br />The detection of the past earthquakes<br />
  20. 20. Facts about earthquake detection<br />
  21. 21. The number of tweets on earthquakes<br />
  22. 22. E-mail<br />The location is obtained by a registered location on the user profile on twitter.<br />Dear Alice,<br />We have just detected an earthquake<br />around Chiba. Please take care.<br />Best,<br />Toretter Alert System<br />
  23. 23. Another prototype<br />Rainbow information<br />Using a similar approach used for detecting earthquakes.<br />Not so time-sensitive<br />Rainbows can be found in various regions simultaneously<br />World rainbow map<br />No agency is reporting rainbow information<br />
  24. 24. Another plan<br />Reporting sighting of celebrities<br />Map of celebrities found in cities<br />We specifically examine the potential uses of the technology. Of course, we should be careful about privacy issues<br />
  25. 25. Related works<br />Tweettronics<br />Analysis of tweets about brands and products for marketing purposes<br />Web2express Digest<br />Auto-discovering information from twitter streaming data to find real-time interesting conversations<br />
  26. 26. Conclusion<br />Earthquake prediction system<br />The system might be designated as semantic twitter<br />Twitter enable us to develop an advanced social medium<br />
  27. 27. Thank you<br />
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