Saku textmining 4 textmining for social

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For society to be applied to text mining
Using the UI tools

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  • Saku textmining 4 textmining for social

    1. 1. GUI @stiq2011/07/16 1
    2. 2. • @stiq••• web• 2
    3. 3. ••• GUI 3
    4. 4. • 4
    5. 5. 5
    6. 6. 6
    7. 7. 20067
    8. 8. 20068
    9. 9. • 9
    10. 10. ••• 10
    11. 11. •• 11
    12. 12. ••outlook temperature humidity windy playsunny 85 85 FALSE nosunny 80 90 TRUE noovercast 83 86 FALSE yesrainy 70 96 FALSE yesrainy 68 12 80 FALSE yes
    13. 13. • ...• 5 13
    14. 14. • 14
    15. 15. • 15
    16. 16. •• 16• 121•• -> 16
    17. 17. • 5000•• 3 15000• 17
    18. 18. 18
    19. 19. • (mecab,sen)••• 19
    20. 20. 20
    21. 21. • tf-idf•• Harman, Sparck Jones 21
    22. 22. • C4.5•• SVM•• 22
    23. 23. ••• 23
    24. 24. • 8• 24
    25. 25. ••• GUI 25
    26. 26. • 26
    27. 27. •••• 27
    28. 28. ••• web• web 28
    29. 29. • • web • • Amazon Mechanical • • OCR 29
    30. 30. •••• 30
    31. 31. •••• 31
    32. 32. • 32
    33. 33. • 33
    34. 34. 34
    35. 35. 35
    36. 36. • • • web • OCR • 36
    37. 37. ••• GUI 37
    38. 38. GUI 38
    39. 39. GUI 39
    40. 40. GUIJavaJava 40
    41. 41. GUI Python 41
    42. 42. • csv weka arff• arff• 42
    43. 43. outlook temperature humidity windy playsunny 85 85 FALSE nosunny 80 90 TRUE noovercast 83 86 FALSE yesrainy 70 96 FALSE yesrainy 68 80 FALSE yes 43
    44. 44. 44
    45. 45. 45
    46. 46. 46
    47. 47. 47
    48. 48. 48
    49. 49. weka• arff• 49
    50. 50. outlook temperature humidity windy playsunny 85 85 FALSE nosunny 80 90 TRUE noovercast 83 86 FALSE yesrainy 70 96 FALSE yesrainy 68 80 FALSE yes 50
    51. 51. arff 51
    52. 52. Time taken to build model: 0.01 seconds === Stratified cross-validation === === Summary ===Correctly Classified Instances 9 64.2857 %Incorrectly Classified Instances 5 35.7143 % Kappa statistic 0.186 Mean absolute error 0.2857 Root mean squared error 0.4818 Relative absolute error 60 % Root relative squared error 97.6586 % Total Number of Instances 14 52
    53. 53. === Detailed Accuracy By Class ===TP Rate FP Rate Precision Recall F-Measure ROC Area Class 0.778 0.6 0.7 0.778 0.737 0.789 yes 0.4 0.222 0.5 0.4 0.444 0.789 no === Confusion Matrix === a b <-- classified as 7 2 | a = yes 3 2 | b = no 53
    54. 54. •• GUI 54
    55. 55. • 55
    56. 56. • Weka in : http:// www.mkc.zaq.ne.jp/eabeh309/weka/• #sakuTextMining - hamadakoichi blog : http://d.hatena.ne.jp/ hamadakoichi/20110416/p1 56
    57. 57. • MySQL TF-IDF 2 : http://txqz.net/blog/ 2006/12/19/2347• Japanese VoteMatch Working Group ― ― : http://votematch.jpn.org/ votematchwg/japanese/jreference.html 57

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