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Real-time Issue Data Analysis Using TextRank Algorithm
I N T R O D U C E M Y S E L F
JADE YEOM;
Software Engineer & UX/UI Designer
1999.11.21 



“EXPERIMENT, FAIL, LEARN, REPEAT.”
Education


February, 2018 - 14 

1 

February, 2015 -
Careers


April, 2016 - Founder



March, 2016 ~ April, 2017 - Software Engineer

Awards
30 

Oct, 2013 - (SchoolSOS)

31 

October, 2014 - (QuickTask)

SKPlanet Smarteen App Challenge 2015

August, 2015 - ( do:learn)

2016 UNIST 

August, 2016 - 

8 

July, 2017 - ( )
E X P E R I M E N T,
F A I L ,
L E A R N ,
R E P E AT.
1
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* 2014
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, .
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A
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.
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A
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TextRank: Bringing Order into Texts Rada Mihalcea and Paul Tarau
3
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2017-07-10
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2017-07-10
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3.8
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.
2017-07-10
14:18:41
2017-07-10
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2.4
2015
.
.
2017-07-10
08:18:41
2017-07-10
10:12:30
5.3
T E X T R A N K A L G O R I T H M
5
R E S U LT S
F U T U R E R E S E A R C H A N D E X P E C T E D E F F E C T S
.
https://rankr.kr
https://github.com/endlessdev/summarizer
F U T U R E R E S E A R C H A N D E X P E C T E D E F F E C T S
Real-time Issue Data Analysis Using TextRank Algorithm
Real-time Issue Data Analysis Using TextRank Algorithm
THANK YOU
FOR ATTENTION.

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텍스트 랭크 알고리즘을 이용한 실시간 이슈 데이터 분석법

  • 1. Real-time Issue Data Analysis Using TextRank Algorithm
  • 2. I N T R O D U C E M Y S E L F JADE YEOM; Software Engineer & UX/UI Designer 1999.11.21 “EXPERIMENT, FAIL, LEARN, REPEAT.” Education February, 2018 - 14 1 February, 2015 - Careers April, 2016 - Founder March, 2016 ~ April, 2017 - Software Engineer Awards 30 Oct, 2013 - (SchoolSOS) 31 October, 2014 - (QuickTask) SKPlanet Smarteen App Challenge 2015 August, 2015 - ( do:learn) 2016 UNIST August, 2016 - 8 July, 2017 - ( ) E X P E R I M E N T, F A I L , L E A R N , R E P E AT.
  • 3. 1
  • 4. P R O B L E M S A N D R E S E A R C H M E T H O D S
  • 5. P R O B L E M S A N D R E S E A R C H M E T H O D S
  • 6. 1 500   . P R O B L E M S A N D R E S E A R C H M E T H O D S
  • 7. ? P R O B L E M S A N D R E S E A R C H M E T H O D S ?
  • 8. * 2014 P R O B L E M S A N D R E S E A R C H M E T H O D S
  • 9. , . ? P R O B L E M S A N D R E S E A R C H M E T H O D S !
  • 10. P R O B L E M S A N D R E S E A R C H M E T H O D S 1 2 3
  • 11. 2
  • 12. PA G E R A N K A L G O R I T H M (Vertex) , (Edge) (In graph theory)
  • 13. A 0.25 B 0.25 C 0.25 D 0.25 PA G E R A N K A L G O R I T H M A-D / B-C,D / C-B,D / D-A,B,C . A D 0.25 B C, D 0.125 C B, D 0.125 D A, B, C 0.08333 ( )
  • 14. PA G E R A N K A L G O R I T H M A 0.083 B 0.208 C D 0.50.208 A-D / B-C,D / C-B,D / D-A,B,C . A D 0.25 B C, D 0.125 C B, D 0.125 D A, B, C 0.08333 ( )
  • 15. T E X T R A N K A L G O R I T H M A 0.083 B 0.208 C D 0.50.208 , . , . TextRank: Bringing Order into Texts Rada Mihalcea and Paul Tarau
  • 16. 3
  • 17. T E X T R A N K A L G O R I T H M (Schema)
  • 18. T E X T R A N K A L G O R I T H M 30
  • 19. 4
  • 20. T E X T R A N K A L G O R I T H M 7 1,000 7 KSCY 20 21 . , , 1,000 . ( , ) ( , 2 ) / , , . 20 . KSCY , , , , - , Global Session 9 30 . . , , 1 11 (www.kscy.kr) . KSCY , , . 9 (Sentence) 211 (Noun) .
  • 21. T E X T R A N K A L G O R I T H M , , 1 11 (www.kscy.kr) . B , , 1,000 . A SENTENCE SENTENCE , , , , , , , , , , , , , , , , , , , , , , A NOUNS , , , , , , , , , , , , , , , , , , , , , , B NOUNS
  • 22. ? ! T E X T R A N K A L G O R I T H M
  • 23. * (Cosine Similarity) (Structural Equivalence) T E X T R A N K A L G O R I T H M
  • 24. T E X T R A N K A L G O R I T H M
  • 25. T E X T R A N K A L G O R I T H M , , 1,000 . A SENTENCE , , 1 11 (www.KSCY.kr) . B SENTENCE 0.60000 .
  • 26. TR 3 , , 1,000 0.16804301901203098 4 ( , ) ( , 2 ) / , , 0.12720921437593927 2 7 KSCY 20 21 . 0.12403895720955871 8 , , 1 11 (www.KSCY.kr) . 0.12161567732116019 9 KSCY , , 0.11094304196222915 5 KSCY , , , , - , Global Session 9 30 0.09781542856211028 5 20 0.09671097626842523 7 0.08109264598251792 1 7 1,000 0.07253103930602833 T E X T R A N K A L G O R I T H M
  • 27. 7 1,000 7 KSCY 20 21 . , , 1,000 . ( , ) ( , 2 ) / , , . 20 . KSCY , , , , - , Global Session 9 30 . . , , 1 11 (www.KSCY.kr) . KSCY , , . 30% 7 KSCY 20 21 . , , 1,000 . ( , ) ( , 2 ) / , , . 30% T E X T R A N K A L G O R I T H M
  • 28. T E X T R A N K A L G O R I T H M , . ' ' . 10 SNS  " # # " . . . (11 ) ' ' 1 6 vs MVP,  8  vs  .
  • 29. T E X T R A N K A L G O R I T H M TR 2 10 SNS " # # " , , , , , , , , , , 0.3644289912306943 1 ' ' . , , , , , , , , , 0.17644170069290174 3 . , , , , , , , 0.17348340763075204 5 (11 ) ' ' 1 6 vs MVP,  8  vs  , , , , , , , , , , , 0.1446443616177518 4 , , , , , , 0.14100153882790015
  • 30. / (Jaccard Transformed Index For Specific Union) T E X T R A N K A L G O R I T H M ' ' . A SENTENCE (11 ) ' ' 1 6 vs MVP,  8  vs  B SENTENCE 0.04347826 0.13043478
  • 31. T E X T R A N K A L G O R I T H M TR 5 (11 ) ' ' 1 6 vs MVP,  8  vs  , , , , , , , , , , , , , 0.36405889831533783 1 ' ' . , , , , , , , , , 0.20943504244072794 4 , , , , , , , 0.14793614614904468 3 , , , , , , , , , , , 0.14014931482237628 2 10 SNS  " # # " . , , , , , , 0.13842059827251335
  • 32. “ 5 . HQ .” “ 1 .” “ 5 .” 1. “ 5 , 1 .” 2. T E X T R A N K A L G O R I T H M
  • 33. “ 5 , 1 . .” 3. . T E X T R A N K A L G O R I T H M
  • 34. 4. , . keyword summary_issue started_at ended_at rank_avg . . 2017-07-10 16:19:14 2017-07-10 20:32:30 3.8 . . ' ' . 2017-07-10 14:18:41 2017-07-10 16:19:00 2.4 2015 . . 2017-07-10 08:18:41 2017-07-10 10:12:30 5.3 T E X T R A N K A L G O R I T H M
  • 35. 5
  • 36. R E S U LT S
  • 37. F U T U R E R E S E A R C H A N D E X P E C T E D E F F E C T S .
  • 38. https://rankr.kr https://github.com/endlessdev/summarizer F U T U R E R E S E A R C H A N D E X P E C T E D E F F E C T S
  • 39. Real-time Issue Data Analysis Using TextRank Algorithm
  • 40. Real-time Issue Data Analysis Using TextRank Algorithm THANK YOU FOR ATTENTION.