Explainability in Irony Detection

Date

2021-09-05

Author

Buyukbas, Ege Berk
Dogan, Adnan Harun
Ozturk, Aslı Umay
Karagöz, Pınar

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Irony detection , Explainability, Sentiment analysis, Neural models

URI

http://dx.doi.org/10.1007/978-3-030-86534-4_14
https://hdl.handle.net/11511/95420

DOI

https://doi.org/10.1007/978-3-030-86534-4_14

Conference Name

International Conference on Big Data Analytics and Knowledge Discovery (DAWAK 2021)

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

E. B. Buyukbas, A. H. Dogan, A. U. Ozturk, and P. Karagöz, “Explainability in Irony Detection,” presented at the International Conference on Big Data Analytics and Knowledge Discovery (DAWAK 2021), 2021, Accessed: 00, 2022. [Online]. Available: http://dx.doi.org/10.1007/978-3-030-86534-4_14.