Explainability in Irony Detection


Buyukbas E. B., Dogan A. H., Öztürk A. U., Karagoz P.

International Conference on Big Data Analytics and Knowledge Discovery, 27 September 2021, pp.152-157 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1007/978-3-030-86534-4_14
  • Page Numbers: pp.152-157
  • Keywords: Irony detection, Explainability, Sentiment analysis, Neural models, SHAP, LIME
  • Middle East Technical University Affiliated: Yes

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.