%0 Conference Paper %B Discovery Science %D 2020 %T Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars %A Lampridis, Orestis %A Riccardo Guidotti %A Salvatore Ruggieri %E Appice, Annalisa %E Tsoumakas, Grigorios %E Manolopoulos, Yannis %E Matwin, Stan %X We present xspells, a model-agnostic local approach for explaining the decisions of a black box model for sentiment classification of short texts. The explanations provided consist of a set of exemplar sentences and a set of counter-exemplar sentences. The former are examples classified by the black box with the same label as the text to explain. The latter are examples classified with a different label (a form of counter-factuals). Both are close in meaning to the text to explain, and both are meaningful sentences – albeit they are synthetically generated. xspells generates neighbors of the text to explain in a latent space using Variational Autoencoders for encoding text and decoding latent instances. A decision tree is learned from randomly generated neighbors, and used to drive the selection of the exemplars and counter-exemplars. We report experiments on two datasets showing that xspells outperforms the well-known lime method in terms of quality of explanations, fidelity, and usefulness, and that is comparable to it in terms of stability. %B Discovery Science %I Springer International Publishing %C Cham %8 2020// %@ 978-3-030-61527-7 %G eng %U https://link.springer.com/chapter/10.1007/978-3-030-61527-7_24 %R https://doi.org/10.1007/978-3-030-61527-7_24