Use Active Learning to Construct Japanese Emoji Emotion Database

Abstract

Emojis are now frequently used in online communication, which express rich meaningful information and emotional messages. However, communication will fail if the meaning of different Emojis is not well understood, especially for the speakers of different languages and those from different countries/regions. There are very few researches about the Emoji dataset currently, since the process of building an Emoji database is labor-intensive and time-consuming. To solve this problem, we propose an active learning-based framework for building Japanese text datasets containing Emoji. This approach aims to achieve fast and balanced labeling of data given a small and unevenly distributed source of Emoji data. The active learning algorithm selects unlabeled data with high information content for manual labeling and updates the model parameters with the manually labeled data, in which way a large Emoji database is iteratively constructed. The constructed Japanese Emoji database contains hundred types of Emojis, with at least hundred pieces of Our experiment suggests that the Emoji dataset can be efficiently constructed with balanced data and the result dataset can provide rich information for text emotion classification, by rendering an accuracy of over 82%.