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This paper aims to estimate electricity demand functions in Japan’s residential sector.

The authors use a partial adjustment model and empirically analyze regional residential electricity demand by using data on 47 Japanese prefectures.

The results reveal that the price elasticity of residential electricity demand during the analytical period (1990-2010) is remarkably different among prefectures, depending on the magnitude of floor space per household. In addition, this study finds that price elasticity is high compared with income elasticity, implying that residential electricity demand changes with rates. Furthermore, an analysis of factors influencing electricity demand in the residential sector shows that increasing electricity demand growth in each region can be attributable mainly to declining electricity rates and increasing number of households.

These results suggest that monitoring the electricity rates and the number of households is important for forecasting future residential electricity demand at region.

The study considers the impact of the number of households on overall electricity demand and identifies other factors contributing to growth in residential electricity demand. The findings can be used to derive projections for future electricity demand.

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.

Comparative education research in Japan is strongly oriented toward emphasizing fieldwork, unlike Western methodologies that aim for theorization. For this reason, it is sometimes regarded as peripheral research without a theorizing orientation or as a counterstrategy to Western research. This study examines why Japanese comparative education research emphasizes fieldwork, focusing on discussions at the Japanese Society of Comparative Education from the 1990s to the present, and considers whether the discussion far from aimed at theorizing. It can be said that Japanese comparative educational research, while characterized by a field-oriented orientation, has been trying to analyze the subject with sincerity through more in-depth fieldwork and is aware of the back and forth between theorizing and differentiation. Furthermore, recently, an international, agenda-based approach and the concept of transboundary fieldwork based on triangulation and Border Studies as a new way of looking at the field itself have also emerged. Therefore, it can be said that Japanese comparative educational research, while characterized by a field-oriented orientation, is increasingly aiming for a multilayered and relative analysis of the field, which is an argument autonomously derived from a focus on the field rather than being a strategy or a challenge to Western universalization-oriented methodologies.