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Stacked denoising autoencoders for sentiment analysis: a review
Author(s) -
Sagha Hesam,
Cummins Nicholas,
Schuller Björn
Publication year - 2017
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1212
Subject(s) - overfitting , computer science , artificial intelligence , machine learning , sentiment analysis , field (mathematics) , domain (mathematical analysis) , noise (video) , noise reduction , domain adaptation , deep learning , support vector machine , artificial neural network , image (mathematics) , mathematical analysis , mathematics , classifier (uml) , pure mathematics
Deep learning has been shown to outperform numerous conventional machine learning algorithms (e.g., support vector machines) in many fields, such as image processing and text analyses. This is due to its outstanding capability to model complex data distributions. However, as networks become deeper, there is an increased risk of overfitting and higher sensitivity to noise. Stacked denoising autoencoders ( SDAs ) provide an infrastructure to resolve these issues. In the field of sentiment recognition from textual contents, SDAs have been widely used (especially for domain adaptation), and have been consistently refined and improved through defining new alternate topologies as well as different learning algorithms. A wide selection of these approaches are reviewed and compared in this article. The results coming from the reviewed works indicate the promising capability of SDAs to perform sentiment recognition on a multitude of domains and languages. WIREs Data Mining Knowl Discov 2017, 7:e1212. doi: 10.1002/widm.1212 This article is categorized under: Algorithmic Development > Text Mining Technologies > Machine Learning