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

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

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
• Electronic ISSN: 2376-5992
• Publisher: PeerJ
• Peer Reviewed: Peer Reviewed
• Volume: 6
• Article Number: e252
• DOI: https://doi.org/10.7717/peerj-cs.252
• Public URL: https://durham-repository.worktribe.com/output/1269080