Filtrado inteligente de noticias basado en Deep Learning

Lopera Martínez, Ana Isabel (2018). Filtrado inteligente de noticias basado en Deep Learning. Thesis (Master thesis), E.T.S. de Ingenieros Informáticos (UPM).

Description

Title: Filtrado inteligente de noticias basado en Deep Learning
Author/s:
  • Lopera Martínez, Ana Isabel
Contributor/s:
  • Serrano Fernández, Emilio
Item Type: Thesis (Master thesis)
Masters title: Ingeniería Informática
Date: July 2018
Subjects:
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

El análisis de posicionamiento de noticias consiste en identificar la disposición de una noticia con respecto a un hecho o entidad, y supone una tarea fundamental para determinar la veracidad de ésta. Este trabajo presenta una solución sobre el problema presentado en el desaf´ıo de Fake News Challenge, proponiendo un servicio web de filtrado de noticias basado en redes neuronales hacia delante. La herramienta está orientada a usuarios finales y les permitirá obtener la posición de una noticia concreta respecto del titular. Para el desarrollo del clasificador se han comparado representaciones basadas en el método de bag of words y vectores de medias de embedding de word2vec. La representación del titular y el cuerpo de la noticia alimenta un modelo de red hacia delante que realiza una clasificación multiclase. El modelo final consiste en una red neuronal optimizada con tres capas ocultas aplicando regularización L2, tomando como entrada la representaci´on de vectores de medias. Se consigue un resultado de 87% en valor predictivo positivo y 86% en términos de recall.---ABSTRACT---Analysis of news stance consists on addressing the position on a report towards a fact or entity, and takes an important role in the assessment of veracity of the report itself. This project proposes a solution relating to the Fake News Challenge problem, and proposes a web service for news filtering, based on feedforward neural networks. This tool is conceived to end-users and will let them obtain the stance of a text with respect a claim. To develop the classifier, text representations based on bag-of-words and vector embedding mean have been considered. Text representations of headline and article body are used as input for a feedforward neural network trained to make a multiclass classification. The final model proposed consists on a hand-tuned 3-layer feedforward network applying L2 regularization, taking as input two vectors of means of word embeddings. This model achieves a 87% in terms on precision and a 86% in terms on recall.

More information

Item ID: 51620
DC Identifier: http://oa.upm.es/51620/
OAI Identifier: oai:oa.upm.es:51620
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 12 Jul 2018 11:18
Last Modified: 12 Jul 2018 11:18
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