| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560507 |
| ISSN: | 1405-5546 |
| Autors: | Frenda, Simona1 Banerjee, Somnath3 Rosso, Paolo2 Patti, Viviana1 |
| Institucions: | 1Università degli Studi di Torino, Dipartimento di Informatica, Turín, Piamonte. Italia 2Universitat Politécnica de Valencia, Pattern Recognition and Human Language Technology Research Center, València, Valencia. España 3Jadavpur University, Salt Lake, West Bengal. India |
| Any: | 2020 |
| Període: | Abr-Jun |
| Volum: | 24 |
| Número: | 2 |
| Paginació: | 633-643 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | In the last years, the control of online user generated content is becoming a priority, because of the increase of online aggressiveness and hate speech legal cases. Considering the complexity and the importance of this issue, this paper presents an approach that combines the deep learning framework with linguistic features for the recognition of aggressiveness in Mexican tweets. This approach has been evaluated relying on a collection of tweets released by the organizers of the shared task about aggressiveness detection in the context of the Ibereval 2018 evaluation campaign. The use of a benchmark corpus allows to compare the results with those obtained by Ibereval 2018 participant systems. However, looking at the achieved results, linguistic features seem not to help the deep learning classification for this task. |
| Disciplines | Ciencias de la computación |
| Paraules clau: | Inteligencia artificial |
| Keyword: | Deep learning, Aggressiveness automatic detection, Mexican Spanish language, Twitter, Linguistic analysis, Artificial intelligence |
| Text complet: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
