Embedding in hyperbolic space using Japanese word list with semantic principles for sentiment analysis

Abstract

With the rise of machine learning, it is constantly using pre-trained language models on large-scale data in language processing. However, there are still many unresolved issues in terms of descriptive power and ease of application to new data. Linguistic knowledge such as existing concept dictionaries by experts can be considered a solution to these problems. In this paper, we focus on the Japanese word list by semantic principles constructed by the National Institute for Japanese Language and Linguistics and investigate vectorization by embedding it into hyperbolic space, and the ability and the effectiveness of vectorization of the embedding method for sentiment analysis based on the experimental results.