Graph Model for Detection of text unstructured data such as Sarcasm

Axel Rodríguez-García; Armando Jipsion

Autores/as

Axel Rodríguez-García Universidad Tecnológica de Panamá
Armando Jipsion Universidad Tecnológica de Panamá

Palabras clave:

Datos no estructurados, lenguaje de procesamiento natural (NLP), sarcasmo, modelo de grafo

Resumen

El sarcasmo se define a menudo como una ironía verbal para expresar desprecio, un lenguaje matizado con el que los individuos expresan lo contrario de lo que está implícito. Uno de los mayores retos en la construcción de sistemas para detectar los datos no estructurados como el sarcasmo, es la falta de grandes conjuntos de datos anotados. Proponemos un método basado en grafos para la construcción de modelos de lenguaje compacto para la detección del sarcasmo. Este método está diseñado para usar pocos datos, y podría ayudar a detectar fake news, hate speech, etc. Permite además a los investigadores analizar otros aspectos del NLP sin tener que obtener un conjunto de datos gigante. Hoy en día, sigue siendo un desafío identificar claramente los sentimientos y emociones humanos mediante el uso de Inteligencia Artificial. Una exploración detallada de nuestra investigación elevaría las aplicaciones actuales de minería de textos y podría ayudar a comprender mejor el impacto del sarcasmo de los clientes y las partes interesadas, expresado en un entorno de redes sociales. Demostramos que los clasificadores simples construidos a partir del modelo pueden detectar bastante bien el sarcasmo, que generalizan un 5% mejor que los del estado del arte.

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