### Emotion Estimation from Emoticons  
To estimate emotions from emoticons, [PERSON] et al. [1] focused on the appearance of the emoticons as cues. Emoticons are generally composed of character strings. Therefore, when considering emoticons as research objects, we often focus on each character individually. [PERSON] et al. proposed a method to obtain shape information from the entire emoticon by converting its text data to image data and classifying the corresponding emotions using shape information as features. This method allows the capturing of the entire image of the emoticon as a feature, rather than focusing on the individual characters. In addition, this method was shown to be effective for unknown emoticons that were not included in the training data.  
[PERSON] et al. [2] proposed a new system, called AZEmo, which extracts emoticons from Chinese social media and other sources and classifies them into seven emotional categories. The system is based on a kinesics model that divides emoticons into semantic regions (eyes, mouth, etc.). This model has been modified to adapt to the Chinese context.  
[PERSON] et al. [3] focused on emoticons and onomatopoeia in documents to extract the emotions associated with the contents of the entire documents. In their work, [PERSON] et al. investigated the effects of onomatopoeia on the emotions expressed using emoticons; accordingly, they estimated the emotions implied by emoticons. In the case of a combination of an emoticon and onomatopoeia, each of which express different emotions, [PERSON] et al. considered both the possible precedence of only one of the emotions over the others as well as the expression of a completely new emotion.  
[PERSON] et al. [4] identified the correspondences between emoticons and their corresponding linguistic expressions, such as onomatopoeia, and estimated the degree of ambiguity in the general meaning of emoticons. They surveyed users regarding the meanings of emoticons, and applied the results of the survey to quantify the understandability (meaning ambiguity) of emoticons. Based on their results, they applied emotions that can be expressed in everyday vocabulary to identify the ambiguity of emoticons' meanings.  
[PERSON] et al. [5] proposed a method for emotion estimation that learns the character features of emoticons with a convolutional neural network. In their method, the characters of emoticons are converted into character variance representation by word2 vec, and their weights are used as initial parameters that are input to a one-dimensional convolutional neural network. They targeted the five emotions Joy, Surprise, Anger, Sorrow, and Neutral. However, because it is more difficult to capture the meaning of character variants than word variants and because it depends on the quality and quantity of the unsupervised text data, it is necessary to capture the prerequisite knowledge of how each character is used in emoticons.  
Several studies have been conducted with the objective of estimating the emotions of individual emoticons [6]. On the one hand, emoticons are used to express a variety of emotions. On the other hand, when emoticons appear as part of a sentence, it is very difficult to estimate the emotion of the sentence and the emoticon.