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

1. GUI
@stiq
2011/07/16
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outlook temperature humidity windy play
sunny 85 85 FALSE no
sunny 80 90 TRUE no
overcast 83 86 FALSE yes
rainy 70 96 FALSE yes
rainy 68 12 80 FALSE yes

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21. • tf-idf
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web
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38. GUI
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40. GUI
Java
Java
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41. GUI
Python
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42. • csv
weka arff
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43. outlook temperature humidity windy play
sunny 85 85 FALSE no
sunny 80 90 TRUE no
overcast 83 86 FALSE yes
rainy 70 96 FALSE yes
rainy 68 80 FALSE yes
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49. weka
• arff
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50. outlook temperature humidity windy play
sunny 85 85 FALSE no
sunny 80 90 TRUE no
overcast 83 86 FALSE yes
rainy 70 96 FALSE yes
rainy 68 80 FALSE yes
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51. arff
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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
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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
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56. • Weka
in : http://
www.mkc.zaq.ne.jp/eabeh309/weka/
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#sakuTextMining -
hamadakoichi blog : http://d.hatena.ne.jp/
hamadakoichi/20110416/p1
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57. • MySQL TF-IDF 2
: http://txqz.net/blog/
2006/12/19/2347
• Japanese VoteMatch Working Group
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― : http://votematch.jpn.org/
votematchwg/japanese/jreference.html
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