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- とき むらかわ
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1 27 Twitter
2 1 Twitter,,.,.,., Twitter,.,,.,,. URL,,,. BoW(Bag of Words), LSI(Latent Semantic Indexing)., URL,,,,., Accuracy, AUC(Area Under the Curve), Precision, Recall, F,. URL,,,., 3.,,,.
3 [1] Twitter [2] Twitter [3] [4] Twitter [5] [6] URL
4 K Accuracy( ) Precision( ) Recall( ) F1-score(F ) AUC(Area Under the Curve) LSI URL LSI URL [2]
5
6 ROC AUC ROC ROC ROC ( ) (+URL) (+ ) (+ ) (+all)
7 6 2.1 [2] ( ) ( ) ( ) (+URL) (+URL) (+URL) (+ ) (+ ) (+ ) (+ ) (+ ) (+ ) (+all) (+all) (+all) (True Positive) (True Negative) (False Positive) (False Negative)
8 SNS., SNS. Twitter 1,,., Twitter,.,.,.,,. Twitter,. Twitter,. 1.2, 1.,.,,.,.., Twitter,., Twitter,.. Twitter,. ( 1.1).,,..,. 1
9 1 8,,. 1.1: 1.3,..,,., 3 URL,,.
10 ,,,,.,.,,,.,,., Twitter,,, ( ), ,,,,. 2,. 3,. 4, ,. 6,. 7,. 8,.
11 10 2, Twitter. 2.1 [1] [1],.,., ( ).,, ,. 4...,..,..,,,. ( ),.,.
12 ,.,, Twitter [2] Sungho Jeon [2], Twitter 4, SVM,., , ,... URL URL URL. URL URL. URL. URL,.,..,,,.,.,.,. 2.1., URL F SVM.,,. 4 SVM
13 : [2] Step Entered feature Recall Precision F-score URL , F,.,,. 2.3 Twitter [3] [3],, B 2, B. A,. 2.1: ( ).,.,., ( ) ( ), ( ) ( ).,,,.
14 2 13.,.,, [4] [4], ( ).,. A, A ( ). A, A.,,.., ,.,.,. 2.4 Twitter [5] [5],, TV TV.,. 2.2.,,. Twitter., SVM,. 3.,.,,,, 5.,.
15 : 20 1,.,.,.,, , 1.,..,.,, [6] [6], 2,..
16 2 15,.,, 2-1.,,.,.,.,..,.,,,.,,.,.,.,. 2.5 Samuel Brody [7],.,,,.,,.,.
17 (SVM),...,, Bag of Words.,,.,. SVM. K (K-fold cross-validation). 3.2, twitter. twitter , (OR NOT,,, ), ( ),, (, )., 2 /twport 3 Web ,.,, (,, ),,,.,,,,,, ,
18 : 3.4, MeCab 4..,,. 3.5 Bag of Words 5.,., Latent Semantic Indexing(LSI) BoW. 6 Latent Semantic Analysis(LSA). 7
19 : #joqr #npb #allstar 1 HR! #allstar #npb 2 1 #allstar #seibulions #npb #AllStarGame 1 ( )! #allstar 1 #npballstar #allstar #npb #tvasahi 1 MVP #carp #npb #npballstar # 1 #joqr #allstar #npb #npb #npballstar #allstar #hanshin #tigers 0 #joqr #npb #allstar 0 #joqr #npb #allstar #npb #AllStarGame #AllStar 0 #NPB #npballstar #npb 0 #allstar #npb 0 #allstar 0 #allstar 0 #npb 0 #NpbALLSTAR LSI, 2., LSI
20 URL 2.2 [2], URL,., Twitter, URL.. URL, [7],.,.,., , 10. = ( ) 3.2.,,,.,., 3.2,. Yuxin Peng [8], SVM 2,.,, K ,, ,.
21 : K K, K, K-1, 1. K, K-1 1, K.,,. 3.5., 10,. 3.8, Accuracy( ), Precision( ), Recall( ), F1-score(F ), Area Under the Curve(AUC). 3.2: True Positive(TP) False Positive(FP) False Negative(FN) True Negative(TN)
22 : 3.4:
23 : Accuracy( ) Accuracy,.. Accuracy = T P + T N T P + T N + F P + F N Precision( ) Precision,,. Web,, Web Precision. Recall, F.. P recision = T P T P + F P Recall( ) Recall,,. Web, Web, Web Recall. Precision, F..
24 3 23 Recall = T P T P + F N F1-score(F ) F,, Precision Recall.. F 1 score = 2Recall P recision Recall + P recision = 2 T P T P +F N T P T P +F P = T P T P +F N + T P T P +F P 2T P 2T P + F P + F N AUC(Area Under the Curve) Area Under the Curve,, ROC(Receiver Operating Characteristic). ROC, True Positive Rate, False Positeve Rate,,. AUC. 3.6 ROC AUC. ROC AUC. 2, AUC 3.6., ROC AUC. AUC. 3.6: ROC AUC
25 24 4 LSI 2, , (2014/11/14 ), (2015/7/3 ), (2016/1/2 ) 3000.,, 3, 2. 7:3 10, ,, 3 4.1, 4.2., ROC 4.1, 4.2, : 1 Precision Recall F1-score class baseball class wimbledon average Precision Recall F1-score class wimbledon class hakone average Precision Recall F1-score class baseball class hakone average
26 : ROC 4.2: ROC 4.3: ROC
27 : 2 Accuracy AUC , : Accuracy 0.973(+/ ) 0.990(+/ ) 0.990(+/ ) AUC 0.996(+/ ) 0.999(+/ ) 0.998(+/ ) Precision 0.974(+/ ) 0.990(+/ ) 0.990(+/ ) Recall 0.973(+/ ) 0.990(+/ ) 0.990(+/ ) F1-score 0.973(+/ ) 0.990(+/ ) 0.990(+/ ) 4.3, LSI 2, 2. 2.
28 ,,. Python2.7. MeCab. (BoW, LSI) Python gensim 1. Python scikit-learn Twitter, 5500,..,,,,.,,,,,,., 1:10. F (3.6.3 ).,, , SVM,.,... URL,, #. URL URL
29 ( ) ( ). RT 2,., #. # 1.. MeCab...,,,... [5].,,,, :, 5.1.,...,,.,.,.,..
30 :, Bag of Words (BoW ),. Bag of Words Bag of Words Python gensim., N. BoW. 3...,,,,,,,,, 3. 1, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1 BoW. 11, 11 BoW. BoW. 3.5 LSI,
31 URL,,. LSI , 3.1.,,. ( ), ( ).,,.
32 ,, LSI.,,, , 2100,.,. 6.2., %., , LSI 2, % 0.5%, % 5%. 5 Accuracy F. Accuracy , F F, , Accuracy F.. 1.0% 80%, Accuracy 1, F 3,. 1.0%, 80%.
33 : (%) (%) Accuracy Accuracy F F LSI LSI 1 128,.,.., , , Accuracy , Accuracy 32. AUC,. Accuracy 32. F, 32,. 32, F 32., 32.
34 : 1( ) 1 precision recall f1-score 16 precision recall f1-score avg/total avg/total precision recall f1-score 32 precision recall f1-score avg/total avg/total precision recall f1-score 64 precision recall f1-score avg/total avg/total precision recall f1-score 128 precision recall f1-score avg/total avg/total : 2( ) Accuracy AUC Accuracy AUC : ( ) Accuracy AUC Precision Recall f1score (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ )
35 URL URL., , , 6.3., 6.3, , , , , Accuracy F 8, AUC 32, , , , 6.3., , Accuracy, AUC, F 32, , , , 6.3..,. 2, 4.
36 : (+URL) 1 precision recall f1-score 16 precision recall f1-score avg/total avg/total precision recall f1-score 32 precision recall f1-score avg/total avg/total precision recall f1-score 64 precision recall f1-score avg/total avg/total precision recall f1-score 128 precision recall f1-score avg/total avg/total : 2(+URL) Accuracy AUC Accuracy AUC : (+URL) Accuracy AUC Precision Recall f1score (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ )
37 : 1(+ ) 1 precision recall f1-score 16 precision recall f1-score avg/total avg/total precision recall f1-score 32 precision recall f1-score avg/total avg/total precision recall f1-score 64 precision recall f1-score avg/total avg/total precision recall f1-score 128 precision recall f1-score avg/total avg/total : 2(+ ) Accuracy AUC Accuracy AUC : (+ ) Accuracy AUC Precision Recall f1score (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ )
38 : 1(+ ) 1 precision recall f1-score 16 precision recall f1-score avg/total avg/total precision recall f1-score 32 precision recall f1-score avg/total avg/total precision recall f1-score 64 precision recall f1-score avg/total avg/total precision recall f1-score 128 precision recall f1-score avg/total avg/total : 2(+ ) Accuracy AUC Accuracy AUC : (+ ) Accuracy AUC Precision Recall f1score (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ )
39 : 1(+all) 1 precision recall f1-score 16 precision recall f1-score avg/total avg/total precision recall f1-score 32 precision recall f1-score avg/total avg/total precision recall f1-score 64 precision recall f1-score avg/total avg/total precision recall f1-score 128 precision recall f1-score avg/total avg/total : 2(+all) Accuracy AUC Accuracy AUC : (+all) Accuracy AUC Precision Recall f1score (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ ) (+/ )
40 : ( ) 6.2: (+URL)
41 : (+ ) 6.4: (+ ) 6.5: (+all)
42 LSI BoW,. LSI , URL URL URL. 2.2 URL,.,,.,.,.,. URL ,,..., ( ) ( ).,.
43 ,,,..,,...,.,,., ( )( ) ( )( )( ),.,., , 7.2, 7.3, 1, , 0 (1 ) 0,.,,.,,.,. 7.1: #joqr #npb #allstar : #allstar #npb 1 0 #tvasahi #allstar 1 0 #joqr #npb #allstar #allstar #npb ,
44 7 43,. 7.2: #allstar 1 0 #npb #baystars 1 0 #allstar #baystars #allstar 1 1 #carp #npb 1 1 #AllStar #NPB #npballstar #AllStarGame ,,,.,,,,,. 7.3.,.,.,,,. 7.3: #npb 0 1 #allstar #npb 0 1 #allstar 0 1 #allstar #npb 0 1 #allstar 0 1 #AllStarGame #npb 0 1 #allstar 0 1 #npb #AllStarGame 0 1 #npb #allstar MVP #npb
45 [2] 2.1 F F, 0.04., F 0.7,.,.,,.,.,,.,,.,,..
46 ,.,,. URL,,,. BoW, LSI., URL,,,,., Accuracy, AUC, Precision, Recall, F,. URL,,,., 3.,,, , 0-0, 0 (1 ) ,,, , URL,,..,
47 8 46 Like ,., , SVM.,,.,., URL,,,.
48 47,,.,,,.., 2,, 1,,,, 4,,,,,..
49 48 [1].. WISS2010, 41-46, [2] Sungho Jeon, Sungchul Kim, and Hwanjo Yu. Don t Be Spoiled by Your Friends: Spoiler Detection in TV Program Tweets. Seventh International AAAI Conference on Weblogs and Social Media, [3],,,. Twitter. 19, , [4],,,,,. Twitter.. MVE, 110(457), , [5],.. 96 (GN), [6],. SNS. 96 (GN), [7] Samuel Brody, Nicholas Diakopoulos. Cooooooooooooooollllllllllllll!!!!!!!!!!!!!!: using word lengthening to detect sentiment in microblogs. Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, , [8] Yuxin Peng, Jia Yao. AdaOUBoost: adaptive over-sampling and under-sampling to boost the concept learning in large scale imbalanced data sets. Proceedings of the international conference on Multimedia information retrieval. ACM, 2010.
50 8 49, (True Positive, True Negative, False Positive, False Negative, 25 ). 8.1: (True Positive) 2 #allstar #npb #allstar #npb 1 1 #NPB #npballstar 1 1 ( ) #NPB #npballstar 1 1 #npb #allstar #npballstar 1 1 #joqr #npb #allstar 1 1 #allstar 1 1 #joqr #npb #allstar 1 1 #npb 1 1 #npballstar #joqr #npb #allstar #TVasahi # # #allstar #allstargame #npballstar #npb #allstar 1 1 #npb #allstar #npballstar 1 1 #allstar #npb #allstar #npb #seibulions #npb 3!! #allstar #TVasahi MVP #carp #npb #npballstar # 1 1 MVP #allstar 1 1 #joqr #allstar #npb #npb #npb 38 # 1 1 #NPB #npb # #NPB 1 1
51 : (True Negative) #allstar #npb 0 0 #baystars #allstar 0 0 #TVasahi 0 0 # # #allstar #allstargame #npballstar #npb ( ) ( 0 0 )!!!!! #allstar MLB #npb # #npb #AllStarGame #npballstar #allstar #npb!! #allstar 0 0 #BSasahi 0 0 #TVasahi # # #allstar #allstargame #npballstar #npb 2 #TVasahi # # 0 0 #allstar #allstargame #npballstar #npb 0 0 #allstar #AllStarGame #AllStar #NPB 0 0 #npballstar #allstar 0 0 #npb 0 0 #npb 0 0 ww #allstar #AllStar #NPB #npballstar 0 0 #AllStarGame AS.520 #npb 0 0 #npb #npb #npb #allstar 0 0 #allstar 0 0 w #npb 0 0 #allstar 0 0 #TVasahi # # #allstar 0 0 #allstargame #npballstar #npb
52 : (False Positive) #allstar 0 1 #npb #allstar 0 1 #npb #AllStarGame 0 1 #npb 0 1 #allstar #npb #npb #joqr #npb #allstar 0 1 #joqr #npb #allstar 0 1 #allstar 0 1 P #allstar #npb 0 1 #allstar 0 1 PL #allstar 0 1 k #allstar #npb 0 1 #allstar 0 1 #joqr #npb 0 1 #allstar #TVasahi # # 0 1 #allstar #allstargame #npballstar #npb #AllStar #NPB #npballstar #AllStarGame 0 1 #allstar 0 1 #allstar 0 1 #allstar #joqr 0 1 #TVasahi # # #allstar #allstargame 0 1 #npballstar #npb 9 #allstar #npb 0 1! #allstar #npballstar #allstar #npb #tvasahi 0 1 MVP www #allstar 0 1
53 : (False Negative) #ALLSTAR 1 0 #npballstar #AllStarGame #AllStar #NPB #Baseball #NPB HR #npb 1 0 #carp #npb 1 0 #allstar 1 0 #allstar 1 0 HR #allstar 1 0 #allstar #npb 1 0 #allstar 1 0 #allstar #npb #AllStar #NPB #npballstar 1 0 #AllStarGame #allstar 1 0 #allstar 1 0 #allstar 1 0 #sbhawks #allstar 1 0 #allstar #allstar 1 0 MVP #allstar 1 0 MVP ( ) #npb 1 0 #allstar #allstar 1 0 # #NPB #baseball 1 0
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その人工知能は本当に信頼できるのか? 人工知能の性能を正確に評価する方法を開発 概要人工知能 (AI) によるビッグデータ解析は 医療現場や市場分析など社会のさまざまな分野での活用が進み 今後さらなる普及が予想されています また 創薬研究などで分子モデルの有効性を予測する場合にも AI は主要な検証
その人工知能は本当に信頼できるのか? 人工知能の性能を正確に評価する方法を開発 概要人工知能 (AI) によるビッグデータ解析は 医療現場や市場分析など社会のさまざまな分野での活用が進み 今後さらなる普及が予想されています また 創薬研究などで分子モデルの有効性を予測する場合にも AI は主要な検証手段として重視されています ところが 私たちは肝心の AI の性能を正しく評価できているのでしょうか?
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