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Recently, Unicode has been standardized with the penetration of social networking services, the use of emojis has become common. Emojis, as they are also known, are most effective in expressing emotions in sentences. Sentiment analysis in natural language processing manually labels emotions for sentences. The authors can predict sentiment using emoji of text posted on social media without labeling manually. The purpose of this paper is to propose a new model that learns from sentences using emojis as labels, collecting English and Japanese tweets from Twitter as the corpus. The authors verify and compare multiple models based on attention long short-term memory (LSTM) and convolutional neural networks (CNN) and Bidirectional Encoder Representations from Transformers (BERT).

The authors collected 2,661 kinds of emoji registered as Unicode characters from tweets using Twitter application programming interface. It is a total of 6,149,410 tweets in Japanese. First, the authors visualized a vector space produced by the emojis by Word2Vec. In addition, the authors found that emojis and similar meaning words of emojis are adjacent and verify that emoji can be used for sentiment analysis. Second, it involves entering a line of tweets containing emojis, learning and testing with that emoji as a label. The authors compared the BERT model with the conventional models [CNN, FastText and Attention bidirectional long short-term memory (BiLSTM)] that were high scores in the previous study.

Visualized the vector space of Word2Vec, the authors found that emojis and similar meaning words of emojis are adjacent and verify that emoji can be used for sentiment analysis. The authors obtained a higher score with BERT models compared to the conventional model. Therefore, the sophisticated experiments demonstrate that they improved the score over the conventional model in two languages. General emoji prediction is greatly influenced by context. In addition, the score may be lowered due to a misunderstanding of meaning. By using BERT based on a bi-directional transformer, the authors can consider the context.

The authors can find emoji in the output words by typing a word using an input method editor (IME). The current IME only considers the most latest inputted word, although it is possible to recommend emojis considering the context of the inputted sentence in this study. Therefore, the research can be used to improve IME performance in the future.

In the paper, the authors focus on multilingual emoji prediction. This is the first attempt of comparison at emoji prediction between Japanese and English. In addition, it is also the first attempt to use the BERT model based on the transformer for predicting limited emojis although the transformer is known to be effective for various NLP tasks. The authors found that a bidirectional transformer is suitable for emoji prediction.

The purpose of this paper is to define school value and its components. This paper discusses the contents of the school’s value and the structure of the school’s value. This paper also shows findings of educational services and value in high school.

Comprehensive and inclusive questions were posed about “school value” to high-school instructors. A grounded theory approach was then used to analyze the collected data.

This analysis found that school value consists of four core categories: “school,” “students,” “guardians” and “region,” which consist of 13 high-ranking categories believed to represent the substance of school value. In addition, three of the four core categories are linked in a star pattern around the core category of “school.”

This research analyzes the value of the school using the grounded theory approach. The data used for the analysis are interview data on the value of the school. And these analyzes reveal the contents of school value and the structure of school value.

This study discussed school value based on hearing data of faculty members. In the analysis method of this research, each school can find its own value by changing the target and contents of interview.

Study of this school value clarified a leader’s role and action in the creation scene of school value. This finding will enable efficient activities of school leaders. As a result, it can be expected to promote school improvement.

The authors identified the categories forming “school value” and their relative relationships. “School value” emphasizes results co-created by stakeholders.