| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560518 |
| ISSN: | 1405-5546 |
| Autores: | Hernández Castañeda, Ángel1 García Hernández, René Arnulfo2 Ledeneva, Yulia2 Millán Hernández, Christian Eduardo2 |
| Instituciones: | 1Consejo Nacional de Ciencia y Tecnología, Ciudad de México. México 2Universidad Autónoma del Estado de México, Toluca, Estado de México. México |
| Año: | 2020 |
| Periodo: | Jul-Sep |
| Volumen: | 24 |
| Número: | 3 |
| Paginación: | 1229-1239 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | In recent years, with the rise of the Internet, the automatic deception detection in text is an important task to recognize those of documents that try to make people believe in something false. Current studies in this field assume that the entire document contains cues to identify deception; however, as demonstrated in this work, some irrelevant ideas in text could affect the performance of the classification. Therefore, this research proposes an approach for deception detection in text that identifies, in the first instance, key ideas in a document based on a topic modeling algorithm and a proposed automatic extractive text summarization method, to produce a synthesized document that avoids secondary ideas. The experimental results of this study indicate that the proposed method outperform previous methods with standard collections. |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial |
| Keyword: | Clustering algorithms, Topic modeling, Genetic algorithms, Deep learning, Artificial intelligence |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
