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Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling

Al Moubayed, Noura; McGough, Stephen; Awwad Shiekh Hasan, Bashar

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Authors

Stephen McGough

Bashar Awwad Shiekh Hasan



Abstract

The article presents a discriminative approach to complement the unsupervised probabilistic nature of topic modelling. The framework transforms the probabilities of the topics per document into class-dependent deep learning models that extract highly discriminatory features suitable for classification. The framework is then used for sentiment analysis with minimum feature engineering. The approach transforms the sentiment analysis problem from the word/document domain to the topics domain making it more robust to noise and incorporating complex contextual information that are not represented otherwise. A stacked denoising autoencoder (SDA) is then used to model the complex relationship among the topics per sentiment with minimum assumptions. To achieve this, a distinct topic model and SDA per sentiment polarity is built with an additional decision layer for classification. The framework is tested on a comprehensive collection of benchmark datasets that vary in sample size, class bias and classification task. A significant improvement to the state of the art is achieved without the need for a sentiment lexica or over-engineered features. A further analysis is carried out to explain the observed improvement in accuracy.

Citation

Al Moubayed, N., McGough, S., & Awwad Shiekh Hasan, B. (2020). Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling. PeerJ Computer Science, 6, Article e252. https://doi.org/10.7717/peerj-cs.252

Journal Article Type Article
Acceptance Date Dec 23, 2019
Online Publication Date Jan 27, 2020
Publication Date Jan 27, 2020
Deposit Date Mar 4, 2020
Publicly Available Date Mar 4, 2020
Journal PeerJ Computer Science
Publisher PeerJ
Peer Reviewed Peer Reviewed
Volume 6
Article Number e252
DOI https://doi.org/10.7717/peerj-cs.252

Files

Published Journal Article (17 Mb)
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
© 2020 Al Moubayed et al.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.







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