| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560516 |
| ISSN: | 1405-5546 |
| Autores: | Ameer, Iqra1 Ashraf, Noman1 Sidorov, Grigori1 Gómez Adorno, Helena2 |
| Instituciones: | 1Instituto Politécnico Nacional, Centro de Investigación en Computación, Ciudad de México. México 2Universidad Nacional Autónoma de México, Instituto de Investigacin en Matemaáticas Aplicadas y en Sistemas, Ciudad de México. México |
| Año: | 2020 |
| Periodo: | Jul-Sep |
| Volumen: | 24 |
| Número: | 3 |
| Paginación: | 1159-1164 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | Multi-label Emotion Classification is a supervised classification problem that aims to classify multiple emotion labels from a given text. Recently, Multi-label Emotion Classification has appealed to the research community due to possible applications in E-learning, marketing, education, and health care, etc. We applied content-based methods (words and character n-grams) on tweets to show how our purposed content-based method can be used for the development and evaluation of the Multi-label Emotion Classification task. The results achieved after our extensive experimentation demonstrate that content-based word unigram surpassed other content-based features (Multi-label Accuracy = 0.452, MicroF1 = 0.573, MacroF1 = 0.559, Exact Match = 0.141, Hamming Loss = 0.179). |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial |
| Keyword: | Multi-label emotion classification, Content-based methods, Twitter, Artificial intelligence |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
