Abstract
Irony detection is a text analysis problem aiming to detect ironic content. The methods in the literature are mostly for English text. In this paper, we focus on irony detection in Turkish and we analyze the explainability of neural models using Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). The analysis is conducted on a set of annotated sample sentences.
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Buyukbas, E.B., Dogan, A.H., Ozturk, A.U., Karagoz, P. (2021). Explainability in Irony Detection. In: Golfarelli, M., Wrembel, R., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2021. Lecture Notes in Computer Science(), vol 12925. Springer, Cham. https://doi.org/10.1007/978-3-030-86534-4_14
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DOI: https://doi.org/10.1007/978-3-030-86534-4_14
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