Resumen
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.
| Idioma original | Inglés |
|---|---|
| Páginas (desde-hasta) | 1507-1515 |
| Número de páginas | 9 |
| Publicación | Computacion y Sistemas |
| Volumen | 23 |
| N.º | 4 |
| DOI | |
| Estado | Publicada - 1 ene. 2019 |
ODS de las Naciones Unidas
Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible
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ODS 3: Salud y bienestar
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ODS 12: Producción y consumo responsables
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ODS 16: Paz, justicia e instituciones sólidas
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ODS 17: Alianzas para lograr los objetivos
Palabras clave
- Bloom filter
- Disclosure risk
- Information loss
- Privacy
- Sentiment analysis
Huella
Profundice en los temas de investigación de 'PS I love you: Privacy aware sentiment classification'. En conjunto forman una huella única.Citar esto
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