Modelo de Aprendizaje Supervisado para la Identificación Automática de Spam en Entornos de Mensajería Móvil
DOI:
https://doi.org/10.61799/2216-0388.1797Palabras clave:
aprendizaje automatico, clasificación, mensajería móvil , modelo supervisado, procesamiento de texto, Random Forest, Spam, Vectorización de textoResumen
Este artículo presenta un modelo de aprendizaje supervisado para la detección automática de mensajes no deseados (spam) en entornos de mensajería móvil, utilizando datos del repositorio Hugging Face. La metodología incluyó las etapas de limpieza y normalización del texto, traducción al español, análisis exploratorio y representación de los mensajes mediante la técnica de bolsa de palabras (CountVectorizer). Posteriormente, el conjunto de datos fue dividido en particiones de entrenamiento y prueba, y se entrenó un clasificador Random Forest con hiperparámetros ajustados manualmente. Los resultados muestran una exactitud global del 96 %, una precisión de 1.00 para la clase spam y una mejora significativa del F1-score (0.82). Además, la validación cruzada de cinco pliegues obtuvo una media de exactitud del 94.4 %, evidenciando estabilidad en el modelo. En conjunto, estos hallazgos confirman que el modelo propuesto es una alternativa eficaz para la detección de spam en aplicaciones de mensajería móvil.
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Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
