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In this paper, we propose a method for converting Japanese emoticons into emoji. The method creates a model that translates text into emoji using training neural networks with a character-based feature; the model determines the positive/negative/neutral polarity of the emoji from the text. By extracting a feature vector from the hidden layer of the model, we calculate the similarity between the input sentence and the text annotated with emoji in the database; the conversion candidates come from the emoticon in the input text. In comparison tests of the proposed method and a word-level feature method that uses a word distributed representation vector, the emoji emotion polarity-based model achieved a maximum accuracy rate of 90.0%, representing an 8% improvement over the word-level feature method.
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