| Revista: | Computación y sistemas |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000379428 |
| ISSN: | 1405-5546 |
| Autores: | Li, Huayi1 Liu, Bing1 Mukherjee, Arjun2 Shao, Jidong3 |
| Instituciones: | 1University of Illinois, Department of Computer Science, Chicago, Illinois. Estados Unidos de América 2University of Houston, Department of Computer Science, Houston, Texas. Estados Unidos de América 3Dianping Inc., Shanghai. China |
| Año: | 2014 |
| Periodo: | Jul-Sep |
| Volumen: | 18 |
| Número: | 3 |
| Paginación: | 467-475 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Analítico, descriptivo |
| Resumen en inglés | Fake review detection has been studied by researchers for several years. However, so far all reported studies are based on English reviews. This paper reports a study of detecting fake reviews in Chinese. Our review dataset is from the Chinese review hosting site Dianping, which has built a fake review detection system. They are confident that their algorithm has a very high precision, but they don't know the recall. This means that all fake reviews detected by the system are almost certainly fake but the remaining reviews may not be all genuine. This paper first reports a supervised learning study of two classes, fake and unknown. However, since the unknown set may contain many fake reviews, it is more appropriate to treat it as an unlabeled set. This calls for the model of learning from positive and unlabeled examples (or PU-learning). Experimental results show that PU learning not only outperforms supervised learning significantly, but also detects a large number of potentially fake reviews hidden in the unlabeled set that Dianping fails to detect |
| Disciplinas: | Ciencias de la computación, Literatura y lingüística |
| Palabras clave: | Inteligencia artificial, Aprendizaje de máquinas, Revisiones falsas |
| Keyword: | Computer science, Literature and linguistics, Artificial intelligence, Machine learning, Fake reviews |
| Texto completo: | Texto completo (Ver HTML) |
