著作: 中尾 英俊/迪力夏提・吉热拉/[松本 和幸]/[吉田 稔]/[北 研二]/Emotion Recognition from Emoticons using Convolutional Neural Networks/Proceedings of 2nd Technological Competency as Caring in the Health Sciences 2018/[北 研二]
(英) Emotion Recognition from Emoticons using Convolutional Neural Networks
(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.
(英) Proceedings of 2nd Technological Competency as Caring in the Health Sciences 2018
|年月日||必須||2018年 8月 19日|