Twitter Twitter [1] 1 2. Twitter Twitter Twitter 2. 1 [2] [3] [4] [] [6] Twitter 2. 2 [7] [8] [9] [7] [8] OCR URL Google [1] 2. 3 Twitter
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1 DEIM Forum 217 D Twitter 1. Instagram flickr Twitter (1) (2) Web Twitter (1)
2 Twitter Twitter [1] 1 2. Twitter Twitter Twitter 2. 1 [2] [3] [4] [] [6] Twitter 2. 2 [7] [8] [9] [7] [8] OCR URL Google [1] 2. 3 Twitter
3 LDA [11] [12] [13] [13] (1) 3 (2) CBOW word embedding [14] MeCab 1 MeCab Neologd [] 1 Convolutional Neural Network (CNN) AlexNet [16] ImageNet [17] 1 PCA (Principal Component Analysis) [12] / URL Web Web URL Twitter [18] 3 URL Twitter 3. 2
4 [1] Large Margin Nearest Neighbor (LMNN) [19] LMNN K Twitter , bot Web
5 被災者の役に立つ情報 災害時に役に立つ情報 熊本駅前 ホテルニューオー タニの 1 階で 生活用水 飲 み水無料無制限で支給してま す 現在 1 人もいません 移 動できて水が必要な方は 是非 行かれてください 迷子のペット 熊本市 南区 田崎のローソ ンに迷子の犬がいました 拡散お願いします 救助が必要な方はこのよう にして ツイートを 特に 熊本の方で救助が必要な方 は このようにツイートを 出典 : 出典 : 出典 : 地震の情報 マスコミ批判 ロータリーに 群がる報道陣と か本当に邪魔で邪魔で仕方が 無いんですよ ただでさえ運 搬でてんやわ んやしてるのに 搬入スペースをこの人達で半 分無くしてるんですよ 出典 : 地震の被害 再掲 午前 3 時頃の地震で 震度 強を熊本県阿蘇地方 スーパーなうなんだけ で観測しました この地震 による津波の心配はありま どやばい事になってる 出典 : せん 出典 : 応援 いい話 台湾海軍の軍艦 高雄 の 艦長さんが FB にアップさ れていた画像 中略 熊 本縣 加油 早日從地震 災害中走出來 原文 と のメッセージも頂いてい ます 出典 : 自衛隊などの災害出動 避難所で ガラス踏んだ 足指を手当てしましょう か と話しかけたお医者 さん 近くの病院まで連 れて行かれ レントゲン 取って麻酔後摘出し縫合 までしてくれた 後略 出典 : 空輸航空団は本日 日本政 府による熊本地震被災地救 援活動を支援するため 2 機の C-13 と乗員を九州 地方へ派遣しました 大型 車両と千歳基地からの人員 を輸送します 出典 : 図 1 熊本地震における代表的な投稿文脈の投稿例 表 4 代表的な文脈における画像投稿数 のツイートを用いて 1 次元の単語の分散表現を学習した 投 被災者に役立つ情報 6 稿文のベクトル表現と画像のベクトル表現を連結する際には 災害時に役立つ情報 32 画像ベクトルを 1 次元に次元削減した 地震の情報 6 地震の被害 41 迷子のペット 自衛隊などの災害出動 31 述べた方法で 1 件取得した 距離学習の実装は python の いい話 支援や応援への感謝 評価 24 metric-learn ライブラリ 注 2 を用いた 検索対象の画像投稿と 応援 82 クエリの間の投稿文脈ベクトルの距離は 学習した距離行列を マスコミ批判 用いて計算したクエリ内の各投稿との距離を全て足し合わせた 距 離 学 習 クエリの投稿を正例とし 負例は全データセットから 3. 2 で ものとした や 記者の Twitter の投稿における配慮にかけた振る舞いを避 4. 2 実 験 結 果 難するものなど テレビのスクリーンショットや文字ベースの 文脈ごとの性能評価 以下で述べる各手法について 各文脈で画像投稿検索を行っ 画像が多い 熊本地震では #くまモン頑張れ絵 というハッシュタ た 1 つのクエリに用いる画像投稿の数は 3 件とし 各投稿文 グで 熊本への応援のメッセージを伝えるくまモンのイラスト 脈で 4 種類のクエリを用いて検索を行った. このときの 検索 が多く投稿された そのような 熊本への応援メッセージを表 結果の上位 3 件における正答数の平均によって評価を行う 現する投稿 イラストが大部分を占める その結果を表 に示す なお その文脈の正答数と平均の差の 迷子のペットの情報 地震によって逃げたペットを探している 最大値を ± の後に記している 実際の Twitter における運用 人や 逃げてきたペットを預かっている人による その情報を ではリツイートで見かけた投稿をクエリとする機会が多くなる 伝えるための投稿 ことが予測される すなわち リツイートの頻度が高い画像投 応援 いい話 支援や応援への感謝 評価など 被災地で活動する 稿ほどクエリになりやすいと考えられる そこで 本研究にお 自衛隊や支援物資を送ってくれた人への感謝を述べる投稿など いてクエリを選択する際も その文脈の投稿をリツイートの多 自衛隊などの災害出動 い順に並べ 上位から順に重複なく 3 つづつ 4 パターン取得 自衛隊や海上保安庁 在日米軍などに よる災害出動に関する画像投稿 大部分がそれらの期間の公式 した アカウントから投稿されたものである ベースライン 投稿内の文章のみを利用し 距離学習を行わ 投稿文脈ベクトル リツイート回数上位の画像投稿データと同期間の約 67 万件 注 2
6 top3 17.±2. 3.±3. 1.±6..±2. 2.±2. 13.±2. 19.±1.7 2.±2..± ±. 2.7±1.7 6.±3.7 1.±1. 3.±3. 12.±4. 18.±2. 1.±1. 9.7±4. 14.±4. 8.7± ±4.7 1.±1. 6.±3. 16.±3. 2.±1. 3.±3.7.7± ±2.7 4.±2. 6.7±4..±1..7± ±1. 2.±4. 1.±1. 1.± ±1. 6.±3. 13.±3. 1.7±1. 1.±6. 14.±1. 21.±2..± ± ±2. 4.±.7 7.±2. 1.7±1.7 8.±7. 13.±1. 21.±2. 2.±2. 8.± CNN ImageNet Twitter Twitter Twitter Twitter [1] Liu Yang and Rong Jin. Distance metric learning: A comprehensive survey. Michigan State Universiy, Vol. 2, No. 2, 26. [2] Nikhil Rasiwasia, Jose Costa Pereira, Emanuele Coviello, Gabriel Doyle, Gert RG Lanckriet, Roger Levy, and Nuno Vasconcelos. A new approach to cross-modal multimedia retrieval. In Proceedings of the 18th ACM international conference on Multimedia, pp ACM, 21. [3] Yunchao Gong, Qifa Ke, Michael Isard, and Svetlana Lazebnik. A multi-view embedding space for modeling internet images, tags, and their semantics. International journal of computer vision, Vol. 16, No. 2, pp , 214. [4] David M Blei and Michael I Jordan. Modeling annotated data. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pp ACM, 23. [] Fei Yan and Krystian Mikolajczyk. Deep correlation for matching images and text. In Proceedings of the IEEE Con-
7 :, : ference on Computer Vision and Pattern Recognition, pp , 2. [6] Andrej Karpathy, Armand Joulin, and Fei Fei F Li. Deep fragment embeddings for bidirectional image sentence mapping. In Advances in neural information processing systems, pp , 214. [7] Kailong Chen, Tianqi Chen, Guoqing Zheng, Ou Jin, Enpeng Yao, and Yong Yu. Collaborative personalized tweet recommendation. In Proceedings of the 3th international ACM SIGIR conference on Research and development in information retrieval, pp ACM, 212. [8] Wei Feng and Jianyong Wang. Retweet or not?: personalized tweet re-ranking. In Proceedings of the sixth ACM international conference on Web search and data mining, pp ACM, 213. [9] Liangjie Hong, Aziz S Doumith, and Brian D Davison. Cofactorization machines: modeling user interests and predicting individual decisions in twitter. In Proceedings of the sixth ACM international conference on Web search and data mining, pp ACM, 213. [1] Tao Chen, Xiangnan He, and Min-Yen Kan. Context-aware image tweet modelling and recommendation. In Proceedings of the 216 ACM on Multimedia Conference, pp ACM, 216. [11],,,.. (NL), Vol. 212, No. 4, pp. 1 6, 212. [12],,.. In DEIM Forum, 211, 211. [13],.. In DEIM Forum, 214. [14] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space.
8 arxiv preprint arxiv: , 213. [] Sato Toshinori. Neologism dictionary based on the language resources on the web for mecab, 2. [16] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pp , 212. [17] BVLC AlexNet Model. org/bvlc_alexnet.caffemodel. [18],,,.. (NL), Vol. 213, No. 14, pp. 1 8, 213. [19] Kilian Q Weinberger and Lawrence K Saul. Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research, Vol. 1, No. Feb, pp , 29.
Twitter Twitter [5] ANPI NLP 5 [6] Lee [7] Lee [8] Twitter Flickr FreeWiFi FreeWiFi Flickr FreeWiFi 2. 2 Mikolov [9] [10] word2vec word2vec word2vec k
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