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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

A Deep Architecture for Sentiment Analysis of News Articles

verfasst von : Dinh Nguyen, Khuong Vo, Dang Pham, Mao Nguyen, Tho Quan

Erschienen in: Advanced Computational Methods for Knowledge Engineering

Verlag: Springer International Publishing

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Abstract

In this paper, we present a deep architecture to perform aspect-level sentiment analysis for news articles. We combine some neural networks models proposed in various deep learning approaches, aiming at tackling specific issues commonly occurring for news articles. In this paper, we explain why our architecture can handle typically-long and content-specific news articles, which often cause overfitting when trained with neural networks. Moreover, the proposed architecture can also effectively process the case when the subject to be analyzed sentimentally is not the main topic of the concerned article, which is also a common issue when performing aspect-level sentiment processing. Experimental results with real dataset demonstrated advantages of our approach as compared to the existing approaches.

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Vorheriges Kapitel An Improved Algorithm for Mining Top-k Association Rules
Nächstes Kapitel Sentiment Polarity Detection in Social Networks: An Approach for Asthma Disease Management
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Metadaten
Titel
A Deep Architecture for Sentiment Analysis of News Articles
verfasst von
Dinh Nguyen
Khuong Vo
Dang Pham
Mao Nguyen
Tho Quan
Copyright-Jahr
2018
Buch
Advanced Computational Methods for Knowledge Engineering

Print ISBN: 978-3-319-61910-1

Electronic ISBN: 978-3-319-61911-8

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-61911-8_12

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