| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560710 |
| ISSN: | 1405-5546 |
| Autores: | Matsumoto, Kazuyuki1 Matsunaga, Takumi2 Yoshida, Minoru1 Kita, Kenji1 |
| Instituciones: | 1University of Tokushima, Graduate School of Technology Industrial and Social Sciences, Tokushima, Shikoku. Japón 2University of Tokushima, Graduate Schools of Science and Technology for Innovation Division of Science and Technology, Tokushima, Shikoku. Japón |
| Año: | 2022 |
| Periodo: | Abr-Jun |
| Volumen: | 26 |
| Número: | 2 |
| Paginación: | 875-886 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | We propose a method for constructing a dictionary of emotional expressions, which is an indispensable language resource for sentiment analysis in the Japanese. Furthermore, we propose a method for constructing a language model that reproduces emotional similarity between words, which to date has yet not been considered in conventional dictionaries and language models. In the proposed method, we pre-trained sentiment labels for the distributed representations of words. An intermediate feature vector was obtained from the pre-trained model. By learning an additional semantic label on this feature vector, we can construct an emotional semantic language model that embeds both emotion and semantics. To confirm the effectiveness of the proposed method, we conducted a simple experiment to retrieve similar emotional words using the constructed model. The results of this experiment showed that the proposed method can retrieve similar emotional words with higher accuracy than the conventional word-embedding model. |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial |
| Keyword: | Artificial intelligence |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
