Graduate School of Technology, Industrial and Social Sciences, Tokushima University, Tokushima University, 2-1 Minamijyousanjima-cho, Tokushima 770-8506, Japan.

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Abstract—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.

Index Terms—Emoticon, emotion recognition, character embedding, convolutional neural networks.

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Cite: Kazuyuki Matsumoto, Akira Fujisawa, Minoru Yoshida, Kenji Kita, "Emotion Recognition of Emoticons Based on Character Embedding," Journal of Software vol. 12, no. 11, pp. 849-857, 2017.