September 2024
Intermediate to advanced
424 pages
13h 3m
Spanish
book
Planos para el análisis de textos con Python
by Jens Albrecht, Sidharth Ramachandran, Christian Winkler
Content preview from Planos para el análisis de textos con Python
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Capítulo 8. Métodos no supervisados: Modelización de temas y agrupación
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Cuando trabajas con un gran número de documentos, una de las primeras preguntas que quieres hacerte sin leerlos todos es "¿De qué hablan?". Te interesan los temas generales de los documentos, es decir, qué palabras (idealmente semánticas) se utilizan a menudo juntas.
El modelado de temas intenta resolver ese reto utilizando técnicas estadísticas para averiguar temas a partir de un corpus de documentos. Dependiendo de tu vectorización (véase el Capítulo 5), puedes encontrar distintos tipos de temas. Los temas consisten en una distribución de probabilidad de características (palabras, n-gramas, etc.).
Los temas normalmente se solapan entre sí; no están claramente separados. Lo mismo ocurre con los documentos: no es posible asignar un documento de forma única a un único tema; un documento siempre contiene una mezcla de temas diferentes. El objetivo del modelado temático no es principalmente asignar un tema a un documento arbitrario, sino encontrar la estructura global del corpus.
A menudo, un conjunto de documentos tiene una estructura explícita que viene dada por categorías, palabras clave, etc. Si queremos echar un vistazo a la composición orgánica del corpus, el modelado temático ayudará mucho a descubrir la estructura latente.
El modelado de temas se conoce desde hace mucho tiempo y ha ganado ...
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