GDP Bollen 2011 Twitter Twitter 86.7 Bollen 2011 Twitter NTT 2014 Twitter Twiiter Twitter SNS
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1 GDP Bollen 2011 Twitter Twitter 86.7 Bollen 2011 Twitter NTT 2014 Twitter Twiiter Twitter SNS
2 114 SNS 表 1 年別歌詞収集曲数 J POP CD TOP
3 115 Lyrics Master KH Coder 4 PA NA 2 PA NA 3 A 2006 positive negative PA 0.99 NA 0.99 B 2005 A PA NA C Bradburn 1969 Affect Balance ScaleABS A 5 ABS 5 PA 5 NA 10 PA NA 100 ABS ABS 3 PA NA 6 PA NA
4 116 6 PA NA 1 Diffusion Index DI DI PA NA 3. 1 A B C PA NA DI 1 PA NA DI PA NA DI 3 DI 2 1 PA PA NA NA DI DI PA NA DI
5 流行歌歌詞と日本経済 図1 117 年別出現率結果比較 3 経済との関係 それでは本題である 歌詞と経済との関係の分析に移る 表 3 は 12 種の歌詞データ PA 歌詞 NA 歌詞 歌詞 DI それぞれ 4 種 と 15 の経済指標との相関行列である8 表 3 で斜体表記されている組み合わせが 相対的に相関係数の高い 2 変数の組み合わせで ある まず PA 歌詞は消費者態度指数など景気指数との負の相関が比較的強いとわかる ま
6 118 表 2 歌詞データ間の相関行列 NA GDP PAA PAB PAC PA PAA 1 NA PAB PAC GDP NAA NAB NAC NA PA NAA 1 NAB NAC DI NA DIA DIB DIC DIA 1 DIB DIC GDP DI PA NA PA DI DI DI 3 DI 5 p DI 0.92 DI p
7 119 表 3 歌詞と経済指標との相関行列 GDP GDP DI DI DI PA A NA A PA B NA B PA C NA C PA NA DI A DI B DI C DI 表 4 歌詞と経済指標との相関行列 (1 階差分 ) GDP GDP DI DI DI PA A NA A PA B NA B PA C NA C PA NA DI A DI B DI C DI
8 120 表 5 単回帰分析結果 P E 20 DI 表 6 重回帰分析結果 P E 06 DI E 表 7 比較用の単回帰分析結果 P E Newey-West PA NA DI DI3 DI3 4. PA NA PA NA 3 NA NA NA
9 121 NA NA 2 CD 注 1 TOP50 2http:// GET Online 3Lyrics Master Kenichi Maehashi 4KH Coder khc.sourceforge.net/2014/12/01 5 Affect Balance Scale PAPleased Things going your way Proud Excited or interested On top of worldnarestless Bored Depressed or very unhappy Lonely UpsetBradburn A PA 1054 NA 3266 B PA 758 NA 548 C PA 35 NA 49 7http:// html2015/03/10 8 GDP GDP go.jp/data/chouki/index.htm2015/02/ p. 390
10 122 参考文献 ac.jp/index.php?open%20resources%2fjapanese%20sentiment%20polarity%20dictionary 2014/12/ pp Vol. 12 No. 3 pp Vol. 47 No. 2 pp ja.html2014/12/ pp Vol. 2 pp Vol. 3 pp Vol. 4 pp J 15 Bradburn, N. M. 1969The Structure of Psychological Well-Being. Aldine Publishing. 16 Bollen, J., Mao, H., & Zeng, X. 2011Twitter mood predicts the stock market. Journal of Computational Science, 2 1, NTT Twitter Twitter
1 Diffusion Index 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65
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1 INDEX 2 4 9 13 24 29 2 3 4 1000 900 800 700 600 500 400 300 200 100 0 90 91 92 93 94 95 96 97 98 99 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 100% 50% 0% 89 90 91 92 93 94 95 96 97 98 99 5 6 7 8 9
(%) 6. 5. 4. 3. 2. 1.. -1. -2. -3. -4. -5. 21/1-3 22/1-3 23/1-3 24/1-3 18, 17, 16, 15, 1 92 84 76 14, 68 13, 6 12, 52 11, 44 1, 9, 8, 7, 36 28 2 22/1 22/7 23/1 23/7 ( 1 FACT BOOK 24 2 14, 12, 1, 8, 6,
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2011 7 9 28.1 41.4 30.5 35.8 31.9% 32.3 DI 18.2 2.4 8.1 3.5 DI 9.4 32.2 0.0 25.9 2008 1 3 2 3 34.8 65.2 46.753.8 1 2 8.82.9 43.1 10 3 DI 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
3 DI 29 7 1 5 6 575 11 751, 13 1,1,25 6 1,251,5 2 1,51,75 1,752, 1 2,2,25 2,252,5 2,53, 3,3,5 3,5 5 1 15 2 25 3 5 6 575 12 751, 21 1,1,25 27 1,251,5 9 1,51,75 1,752, 1 2,2,25 2 2,252,5 2,53, 2 3,3,5
31, 21% 24, 17% 8, 5% 23, 16% 24, 16% 91, 62% 19, 13% 39, 27% 33, 23% 73 48 57 51 31 1 9 13.0% 7.4% 5.3% 12.5% 17.1% 13.2% 17.9% 4.5% 36.4% 56.5% 40.7% 36.8% 50.0% 67.1% 56.3% 65.8% 75.0% 26.0% 37.0%
2 DI 28 7 1 37 28 4 18 27 11 21 5 2 26 4 5 1 15 2 25 3 35 4 17 7 5 48 76 31 47 17 2 92 12 2 2 4 6 8 1 12 1 2 4 1 12 13 18 19 3 42 57 57 1 2 3 4 5 6 1 1 1 3 4 4 5 5 5.5 1 1.5 2 2.5 3 3.5 4 4.5 5
37 27.0% 26 19.0% 74 54.0% 9 6.4% 13 9.2% 28 19.9% 26 18.4% 37 26.2%. 24 17.0% 99 69 75 59 39 1 6 4.5% 1.4% 7.7% 2.9% 25.0% 17.9% 20.8% 50.0% 41.7% 47.0% 51.4% 54.3% 61.5% 57.1% 55.6% 42.4% 50.0% 58.3%
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