October 2024
Intermediate to advanced
332 pages
10h
Spanish
Content preview from Análisis de Texto Aplicado con Python
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Capítulo 5. Clasificación para el análisis de textos
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Imagina que estuvieras trabajando en uno de los grandes proveedores de correo electrónico a finales de los 90, gestionando un número cada vez mayor de correos electrónicos procedentes de servidores de todo el mundo. La prevalencia y la economía del correo electrónico lo han convertido en una forma primaria de comunicación, y los negocios están en auge. Por desgracia, también lo está el aumento del correo basura. En el extremo más inofensivo del espectro, están los anuncios de productos de Internet, que sin embargo se envían en avalanchas que gravan gravemente tus servidores. Además, como el correo electrónico no está regulado, los mensajes dañinos son cada vez más comunes: cada vez más correos contienen publicidad falsa, esquemas piramidales e inversiones falsas. ¿Qué hacer?
Puedes empezar poniendo en la lista negra las direcciones de correo electrónico o las direcciones IP de los spammers o buscando palabras clave que puedan indicar que un correo electrónico es spam. Desgraciadamente, como es relativamente fácil conseguir un nuevo correo electrónico o dirección IP, los spammers burlan rápidamente incluso tus listas negras mejor elaboradas. Peor aún, te estás dando cuenta de que las listas negras y blancas no hacen un buen trabajo para garantizar que el correo electrónico válido llegue, y los usuarios no están ...
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