| Journal: | Computación y Sistemas |
| Database: | PERIÓDICA |
| System number: | 000423239 |
| ISSN: | 1405-5546 |
| Authors: | Tutubalina, Elena1 Nikolenko, Sergey2 |
| Institutions: | 1Kazan Federal University, Kazan. Rusia 2National Research University Higher School of Economics, Laboratory for Internet Studies, San Petersburgo. Rusia 3Steklov Institute of Mathematics, San Petersburgo. Rusia |
| Year: | 2017 |
| Season: | Abr-Jun |
| Volumen: | 21 |
| Number: | 2 |
| Country: | México |
| Language: | Inglés |
| Document type: | Artículo |
| Approach: | Analítico, descriptivo |
| English abstract | Drug reactions can be extracted from user reviews provided on the Web, and processing this information in an automated way represents a novel and exciting approach to personalized medicine and wide-scale drug tests. In medical applications, demographic information regarding the authors of these reviews such as age and gender is of primary importance; however, existing studies usually assume that this information is available or overlook the issue entirely. In this work, we propose and compare several approaches to automated mining of demographic information from user-generated texts. We compare modern natural language processing techniques, including feature rich classifiers, extensions of topic models, and deep neural networks (both convolutional and recurrent architectures) for this problem |
| Disciplines: | Medicina, Literatura y lingüística, Ciencias de la computación |
| Keyword: | Salud pública, Medicación, Procesamiento de lenguaje natural, Revisiones de usuarios, Análisis de textos |
| Keyword: | Public health, Medication, Natural language processing, User reviews |
| Full text: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
