| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560695 |
| ISSN: | 1405-5546 |
| Autores: | Ojo, Olumide Ebenezer1 Ta, Thang-Hoang2 Gelbukh, Alexander1 Calvo, Hiram1 Sidorov, Grigori1 Adebanji, Olaronke Oluwayemisi1 |
| Instituciones: | 1Instituto Politécnico Nacional, Centro de Investigación en Computación, Ciudad de México. México 2Dalat University, Lam Dong. Vietnam |
| Año: | 2022 |
| Periodo: | Abr-Jun |
| Volumen: | 26 |
| Número: | 2 |
| Paginación: | 1007-1013 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | Hatred spreading through the use of language on social media platforms and in online groups is becoming a well-known phenomenon. By comparing two text representations: bag of words (BoW) and pre-trained word embedding using GloVe, we used a binary classification approach to automatically process user contents to detect hate speech. The Naive Bayes Algorithm (NBA), Logistic Regression Model (LRM), Support Vector Machines (SVM), Random Forest Classifier (RFC) and the one-dimensional Convolutional Neural Networks (1D-CNN) are the models proposed. With a weighted macro-F1 score of 0.66 and a 0.90 accuracy, the performance of the 1D-CNN and GloVe embeddings was best among all the models. |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial |
| Keyword: | Artificial intelligence |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
