Semantically-Informed Graph Neural Networks for Irony Detection in Turkish

Abstract

Social media plays an important role in expressing the thoughts and sentiments of users. Irony is a way of stating a sentiment about something by expressing the opposite of the intended literal meaning. Irony detection is a recent emerging task in low-resource languages, although other tasks related to sentiment, such as sentiment analysis and emotion detection, have been widely tackled. In this study, we investigate Graph Neural Networks (GNNs) for irony detection in Turkish, a low-resource language in sentiment-related tasks. We incorporate semantic information into the GNNs using the Universal Conceptual Cognitive Annotation (UCCA) framework. Extensive experimental results and in-depth analysis show that our models outperform state-of-the-art irony detection models in Turkish. Our UCCA-GAT (UCCA-Graph Attention Network) model achieves an F\textsubscript{1}-score of 94.85% (7.362% gain over the state-of-the-art) on the Turkish-Irony-Dataset and an accuracy of 72.82% (4.39% gain over the state-of-the-art) on the IronyTR Dataset. We also provide a comprehensive analysis of the proposed models to understand their limitations.\footnote{The code will be publicly available after acceptance.