『徳島大学 教育・研究者情報データベース (EDB)』---[学外] /
ID: Pass:

登録内容 (EID=344759)

EID=344759EID:344759, Map:0, LastModified:2018年8月21日(火) 10:18:32, Operator:[松本 和幸], Avail:TRUE, Censor:0, Owner:[松本 和幸], Read:継承, Write:継承, Delete:継承.
種別 (必須): 国際会議 [継承]
言語 (必須): 英語 [継承]
招待 (推奨):
審査 (推奨): Peer Review [継承]
カテゴリ (推奨): 研究 [継承]
共著種別 (推奨): 単独著作 (徳島大学内の単一の研究グループ(研究室等)内の研究 (単著も含む)) [継承]
学究種別 (推奨): 修士課程学生による研究報告 [継承]
組織 (推奨):
著者 (必須): 1. (英) Hidetoshi Nakao (日) 中尾 英俊 (読) なかお ひでとし
役割 (任意):
貢献度 (任意):
学籍番号 (推奨): **** [ユーザ]
[継承]
2. (英) Dilixiati Jirela (日) 迪力夏提・吉热拉 (読) でりしゃち じれら
役割 (任意):
貢献度 (任意):
学籍番号 (推奨): **** [ユーザ]
[継承]
3.松本 和幸 ([徳島大学.大学院社会産業理工学研究部.理工学域.知能情報系.情報工学分野]/[徳島大学.理工学部.理工学科.情報光システムコース.情報工学講座])
役割 (任意):
貢献度 (任意):
学籍番号 (推奨):
[継承]
4.吉田 稔 ([徳島大学.大学院社会産業理工学研究部.理工学域.知能情報系.情報工学分野]/[徳島大学.理工学部.理工学科.情報光システムコース.情報工学講座])
役割 (任意):
貢献度 (任意):
学籍番号 (推奨):
[継承]
5.北 研二 ([徳島大学.大学院社会産業理工学研究部.理工学域.知能情報系.情報工学分野]/[徳島大学.理工学部.理工学科.情報光システムコース.情報工学講座])
役割 (任意):
貢献度 (任意):
学籍番号 (推奨):
[継承]
題名 (必須): (英) Emotion Recognition from Emoticons using Convolutional Neural Networks  (日)    [継承]
副題 (任意):
要約 (任意): (英) In recent years, text communication has been grown steadily so that all kinds of daily contacts could be conducted now through Internet based text messaging services. As an advantage of text-based communication via the Internet, we can quickly interact with anyone wherever we are. At the same time, since it is difficult to express emotions with each other only with text, the chance of adding non-verbal information such as photos to text is increasing. Among the non-verbal information, there are emoticons in expressions that have been developed by Internet bulletin boards, chat, e-mail, etc. In particular, the types of emoticons used in Japan are diverse, even now; various types of emoticons continue to increase. There have been studies on these emoticons until now, which have become increasingly important in recent years. In this paper, we propose a method to classify facial expressions expressed from emoticons by letting deep convolutional neural networks. When recognizing emotions from emoticons, it is not known that seeing emoticons in character units is very effective. Information such as role positioning and coexistence roles is necessary. Characters in emoticons differ depending on font it also gives different impressions depending on context. In order to realize semantic analysis / emotion analysis considering emoticons, it is necessary to provide a corpus that records various kinds of emoticons and a database that gives information such as meanings and emotions expressed by emoticons. However, including all the emoticons is difficult, and since the emoticons are more vague than ordinary language expressions, that is why the construction of the dictionary is delayed. In this paper, we propose a method to classify the emotions of the emoticons by letting the deep convolutional neural network learn the features of the emoticons. By treating the emoticon as an image, the visual features can be captured and it is considered that natural recognition can be performed instead of processing like a character string. In addition, since the emoticon characterizes the facial expression with a character string, there is a problem that the impression is different only when the used fonts are different from each other, and therefore, when different fonts are used, how the classification accuracy is changed, we consider whether to do so.  (日)    [継承]
キーワード (推奨): 1. (英) Emoticon (日) 顔文字 (読) [継承]
2. (英) Convolutional Neural Networks (日) 畳み込みニューラルネットワーク (読) [継承]
3. (英) Emotion recognition (日) 感情認識 (読) [継承]
4. (英) Data augmentation (日) データ拡張 (読) [継承]
発行所 (推奨):
誌名 (必須): (英) Proceedings of 2nd Technological Competency as Caring in the Health Sciences 2018 (日) (読)
ISSN (任意):
[継承]
(必須): [継承]
(必須): [継承]
(必須): 72 72 [継承]
都市 (必須):
年月日 (必須): 西暦 2018年 8月 19日 (平成 30年 8月 19日) [継承]
URL (任意):
DOI (任意):
PMID (任意):
NAID (任意):
WOS (任意):
Scopus (任意):
評価値 (任意):
被引用数 (任意):
指導教員 (推奨): 1.北 研二 ([徳島大学.大学院社会産業理工学研究部.理工学域.知能情報系.情報工学分野]/[徳島大学.理工学部.理工学科.情報光システムコース.情報工学講座]) [継承]
備考 (任意):

標準的な表示

和文冊子 ● Nakao Hidetoshi, Jirela Dilixiati, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Emotion Recognition from Emoticons using Convolutional Neural Networks, Proceedings of 2nd Technological Competency as Caring in the Health Sciences 2018, 72, (都市), Aug. 2018.
欧文冊子 ● Nakao Hidetoshi, Jirela Dilixiati, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Emotion Recognition from Emoticons using Convolutional Neural Networks, Proceedings of 2nd Technological Competency as Caring in the Health Sciences 2018, 72, (都市), Aug. 2018.

関連情報

Number of session users = 3, LA = 0.74, Max(EID) = 376362, Max(EOID) = 1008076.