| Revista: | Computación y sistemas |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000423316 |
| ISSN: | 1405-5546 |
| Autores: | Satapathy, Ranjan1 Chaturvedi, Iti1 Cambria, Erik1 Ho, Shirley S2 Na, Jin Cheon2 |
| Instituciones: | 1Nanyang Technological University, School of Computer Science and Engineering, Nanyang. Singapur 2Nanyang Technological University, Wee Kim Wee School of Communication and Information, Nanyang. Singapur |
| Año: | 2017 |
| Periodo: | Oct-Dic |
| Volumen: | 21 |
| Número: | 4 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Analítico, descriptivo |
| Resumen en inglés | The subjectivity detection is an important binary classification task that aims at distinguishing natural language texts as opinionated (positive or negative) and non-opinionated (neutral). In this paper, we develop and apply recent subjectivity detection techniques to determine subjective and objective tweets towards the hot topic of nuclear energy. This will further help us to detect the presence or absence of social media bias towards Nuclear Energy. In particular, significant network motifs of words and concepts were learned in dynamic Gaussian Bayesian networks, while using Twitter as a source of information. We use reinforcement learning to update each weight based on a probabilistic reward function over all the weights and, hence, to regularize the sentence model. The proposed framework opens new avenues in helping government agencies manage online public opinion to decide and act according to the need of the hour |
| Disciplinas: | Ciencias de la computación, Literatura y lingüística |
| Palabras clave: | Lingüística aplicada, Mensajes de texto, Detección de subjetividad, Procesamiento de lenguaje natural |
| Keyword: | Applied linguistics, Text messages, Natural language processing, Subjectivity detection |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
