| Revista: | Latin-American Journal of Computing (LAJC) |
| Base de datos: | |
| Número de sistema: | 000565061 |
| ISSN: | 1390-9134 |
| Autores: | Klinczak, Marjori N. M1 Kaestner, Celso A. A1 |
| Instituciones: | 1Universidade Tecnologica Federal do Parana, Curitiba, Parana. Brasil |
| Año: | 2016 |
| Volumen: | 3 |
| Número: | 1 |
| Paginación: | 19-26 |
| País: | Ecuador |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | Topic Identification in Social Networks has become an important task when dealing with event detection, particularly when global communities are affected. In order to attack this problem, text processing techniques and machine learning algorithms have been extensively used. In this paper we compare four clustering algorithms – k-means, k-medoids, DBSCAN and NMF (Non-negative Matrix Factorization) – in order to detect topics related to textual messages obtained from Twitter. The algorithms were applied to a database initially composed by tweets having hashtags related to the recent Nepal earthquake as initial context. Obtained results suggest that the NMF clustering algorithm presents superior results, providing simpler clusters that are also easier to interpret. |
| Disciplinas: | Ciencias de la computación, Ciencias de la computación |
| Palabras clave: | Inteligencia artificial, Procesamiento de datos |
| Keyword: | Artificial intelligence, Data processing |
| Texto completo: | Texto completo (Ver PDF) |
