文章基本信息

标题：Emotion Recognition of Emoticons Based on Character Embedding
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作者：Kazuyuki Matsumoto ; Akira Fujisaw ; Minoru Yoshida 等
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2017
卷号：12
期号：11
页码：849-859
DOI：10.17706/jsw.12.11.849-857
出版社：Academy Publisher
摘要：This paper proposes a method for estimating the emotions expressed by emoticons based on a distributed representation of the character meanings of the emoticon. Existing studies on emoticons have focused on extracting the emoticons from texts and estimating the associated emotions by separating them into their constituent parts and using the combination of parts as the feature. Applying a recently developed technique for word embedding, we propose a versatile approach to emotion estimation from emoticons by training the meanings of the characters constituting the emoticons and using them as the feature unit of the emoticon. A cross-validation test was conducted for the proposed model based on deep convolutional neural networks using distributed representations of the characters as the feature. Results showed that our proposed method estimates the emotion of unknown emoticons with a higher F1-score than the baseline method based on character n-grams.
其他关键词：Emoticon, emotion recognition, character embedding, convolutional neural networks.