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Real Time Twitter Trend
Mining System – RT²M
Nigar Gasimli
Index
 About
 System overview
 Case Study
 Conclusions and Future works
About
 Social media data is being generated
in real-time
 using static data to detect
continuously changing social trends
such as users’ interests and public
issues could be ineffective
About
Real time twitter mining system allows:
◦ Crawl and store every textual data
(tweets)
◦ Keep track of social issues by temporal
Topic Modeling
◦ Visualize mention based user networks
System overview of RT²M
System overview of RT²M
Main page of the system
Term Co-occurrence retrieval
 Can display the result with an option
of 100, 500, 1,000, and 2,000 terms
 Co-occurred terms are dynamically
updated and displayed as more
Twitter stream data is received
Visualization of Twitter Users
System visualizes the social network
graph of Twitter users mentioned
together with the query term
User Network Analysis
 Open source visualization tool –
JUNG (Java Universal Network
Graph) Framework
 Voltage clustering algorithm to detect
user community
Similarity Calculation between
two users
 Similarity between two users
comparing terms they use in their
tweets, weighted by their tf-idf index
 TF-IDF = Term Frequency * Inverse
document frequency
Topic Modeling
Text mining techniques
 Multinomial Topic Modeling
 Community Detection for Social
Network Analysis
Multinomial Topic Modeling
 LDA- Latent Dirichey Allocation,
represents documents as mixture of
topics that spit out words with certain
probabilities
 An extensin of LDA, DMR – Dirichlet
multinomial Regression used in this
work
Conclusion
 mine dynamic social trends and
content-based networks generated in
Twitter
 Twitter would be a useful medium for
keeping track of topical trends
 The mention-based user network
provides a basis for identifying any
nodes with high betweenness
Future Work
Sentiment analysis to Twitter data to
observe changes in public opinion and
the formation process of a certain
issue, and ultimately design the
prediction model of social issues on
social media.
Bibliography
 A survey of Topic Modeling in Text
Mining
 Blei, D., Ng, A., and Jordan, M. 2003.
Latent Dirichlet Allocation. Journal of
Machine Learning Research, 3: 993-
1022.
Thank you!
Any Questions?

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Real time twitter trend mining system – rt2 m

  • 1. Real Time Twitter Trend Mining System – RT²M Nigar Gasimli
  • 2. Index  About  System overview  Case Study  Conclusions and Future works
  • 3. About  Social media data is being generated in real-time  using static data to detect continuously changing social trends such as users’ interests and public issues could be ineffective
  • 4. About Real time twitter mining system allows: ◦ Crawl and store every textual data (tweets) ◦ Keep track of social issues by temporal Topic Modeling ◦ Visualize mention based user networks
  • 6. System overview of RT²M Main page of the system
  • 7. Term Co-occurrence retrieval  Can display the result with an option of 100, 500, 1,000, and 2,000 terms  Co-occurred terms are dynamically updated and displayed as more Twitter stream data is received
  • 8. Visualization of Twitter Users System visualizes the social network graph of Twitter users mentioned together with the query term
  • 9. User Network Analysis  Open source visualization tool – JUNG (Java Universal Network Graph) Framework  Voltage clustering algorithm to detect user community
  • 10. Similarity Calculation between two users  Similarity between two users comparing terms they use in their tweets, weighted by their tf-idf index  TF-IDF = Term Frequency * Inverse document frequency
  • 12. Text mining techniques  Multinomial Topic Modeling  Community Detection for Social Network Analysis
  • 13. Multinomial Topic Modeling  LDA- Latent Dirichey Allocation, represents documents as mixture of topics that spit out words with certain probabilities  An extensin of LDA, DMR – Dirichlet multinomial Regression used in this work
  • 14. Conclusion  mine dynamic social trends and content-based networks generated in Twitter  Twitter would be a useful medium for keeping track of topical trends  The mention-based user network provides a basis for identifying any nodes with high betweenness
  • 15. Future Work Sentiment analysis to Twitter data to observe changes in public opinion and the formation process of a certain issue, and ultimately design the prediction model of social issues on social media.
  • 16. Bibliography  A survey of Topic Modeling in Text Mining  Blei, D., Ng, A., and Jordan, M. 2003. Latent Dirichlet Allocation. Journal of Machine Learning Research, 3: 993- 1022.

Notas do Editor

  1. Document modelingi deyeceksen birinci, sonra topic modeling de onlardan biridi deyecekksen