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.For further resources related to this article, please visit the WIREs website.