| Revue: | Computación y Sistemas |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000457249 |
| ISSN: | 1405-5546 |
| Autores: | Alatrista Salas, Hugo1 Cordero, Hugo2 Nunez del Prado, Miguel1 |
| Instituciones: | 1Universidad del Pacífico, Lima. Perú 2Universidad Nacional Mayor de San Marcos, Lima. Perú |
| Año: | 2019 |
| Periodo: | Oct-Dic |
| Volumen: | 23 |
| Número: | 4 |
| Paginación: | 1507-1515 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Aplicado, descriptivo |
| Resumen en inglés | At first glance, one might think that people are aware of the availability of comments or posts on social networks. Therefore, one may believe that people do not share sensitive information on public social networks. Nonetheless, people's posts sometimes reveal susceptible information. These posts include mentions the use of drugs or alcohol, sexual preferences, intimate confessions and even serious medical conditions like cancer or HIV. Such privacy leaks could cost someone to get fired or even worse to be a victim of denial insurance or bad credit evaluations. In this paper, we propose a complete process to perform a privacy-preserving sentiment analysis trough Bloom filters. Our approach shows an accuracy difference between 1% and 3% less than their classic sentiment analysis task counter part while guarantying a private aware analysis |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial, Procesamiento de datos, Redes, Privacidad, Análisis de sentimiento, Divulgación, Pérdida de información, Filtro de Bloom |
| Keyword: | Artificial intelligence, Data processing, Networks, Privacy, Sentiment analysis, Disclosure, Information loss, Bloom filter |
| Texte intégral: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
