Using Personality Recognition Techniques to Improve Bayesian Spam Filtering

• Autores: Urko Zurutuza Ortega , José María Gómez Hidalgo , Enaitz Ezpeleta Gallastegui
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 57, 2016, págs. 125-132
• Idioma: inglés
• Títulos paralelos:
  • Uso de Técnicas de Reconocimiento de la Personalidad para Mejorar el Filtrado Bayesiano de Spam
• Enlaces
  • Texto completo
• Resumen
  • español

    Millones de usuarios se ven afectados por las campanas de envío de correos electrónicos no deseados al día. Durante los últimos años diferentes técnicas de detección de spam han sido desarrollados por investigadores, obteniendo especialmente buenos resultados con algoritmos de aprendizaje automático. En este trabajo presentamos una base para un nuevo método de filtrado de spam. Durante el estudio hemos validado la hipótesis de que las técnicas de reconocimiento de personalidad pueden ayudar a mejorar el filtrado Bayesiano de spam. Usando estas técnicas de filtrado, añadimos la característica de personalidad a cada correo, y después comparamos los resultados del filtrado Bayesiano de spam con y sin personalidad, analizando los resultados en términos de exactitud. En un segundo experimento, combinamos las características de personalidad y polaridad de cada mensaje, y comparamos los resultados. Al final, conseguimos mejorar los resultados del filtrado Bayesiano de spam, alcanzando el 99,24% de exactitud, y reduciendo el número de falsos positivos.

  • English

    Millions of users per day are affected by unsolicited email campaigns. During the last years several techniques to detect spam have been developed, achieving specially good results using machine learning algorithms. In this work we provide a baseline for a new spam filtering method. Carrying out this research we validate our hypothesis that personality recognition techniques can help in Bayesian spam filtering. We add the personality feature to each email using personality recognition techniques, and then we compare Bayesian spam filters with and without personality in terms of accuracy. In a second experiment we combine personality and polarity features of each message and we compare all the results. At the end, the top ten Bayesian filtering classifiers have been improved, reaching to a 99.24% of accuracy, reducing also the false positive number.

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