Explainability in Irony Detection

Buyukbas E. B., Dogan A. H., Ozturk A. U., KARAGÖZ P.

International Conference on Big Data Analytics and Knowledge Discovery (DAWAK 2021), 27 Eylül 2021, ss.152-157 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1007/978-3-030-86534-4_14
  • Sayfa Sayıları: ss.152-157
  • Anahtar Kelimeler: Irony detection, Explainability, Sentiment analysis, Neural models, SHAP, LIME
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.