Explainability in Irony Detection

Buyukbas, Ege Berk
Dogan, Adnan Harun
Ozturk, Aslı Umay
Karagöz, Pınar
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.
International Conference on Big Data Analytics and Knowledge Discovery (DAWAK 2021)


Irony detection on microposts with limited set of features
Taslioglu, Hande; Karagöz, Pınar (2017-04-04)
Detecting irony in texts attracts computer scientists' attention as a recent research problem. Automatic detection of irony on microblog texts, i.e., microposts, poses additional challenges. Microposts have limited number of characters, and generally include typing errors, therefore traditional methods of text mining cannot be applied easily. This study aims to automatically detect irony in microposts. The proposed solution is based on supervised learning through a limited set of features extracted from the...
Word Embedding Based Event Detection on Social Media
Ertugrul, Ali Mert; Velioglu, Burak; Karagöz, Pınar (2017-06-23)
Event detection from social media messages is conventionally based on clustering the message contents. The most basic approach is representing messages in terms of term vectors that are constructed through traditional natural language processing (NLP) methods and then assigning weights to terms generally based on frequency. In this study, we use neural feature extraction approach and explore the performance of event detection under the use of word embeddings. Using a corpus of a set of tweets, message terms...
Effective training set sampling strategy for SVDD anomaly detection in hyperspectral imagery
Ergul, Mustafa; Sen, Nigar; Okman, O. Erman (2014-05-07)
Anomaly detection (AD) is an important application for target detection in remotely sensed hyperspectral data. Therefore, variety kinds of methods with different advantages and drawbacks have been proposed for past two decades. Recently, the kernelized support vector data description (SVDD) based anomaly detection approaches has become popular as these methods avoid prior assumptions about the distribution of data and provides better generalization to characterize the background. The global SVDD needs a tra...
Effect of Using Regression in Sentiment Analysis
Onal, Itir; Ertuğrul, Ali Mert (2014-04-25)
In this study, the effect of using regression on sentiment classification of Twitter data was analyzed. In other words, whether the strength of sentiment better discriminates the classes or not. Since our dataset includes class confidence scores rather than discrete class labels, regression analysis was employed on each class separately. Then, each tweet was assigned the class whose estimated confidence score is maximum among others after regression. The feature set used includes unigrams, POS tags, emotico...
Deep convolutional neural networks for airport detection in remote sensing images
Budak, Umit; Sengur, Abdulkadir; Halıcı, Uğur (2018-05-05)
This study investigated the use of deep convolutional neural networks (CNNs) in providing a solution for the problem of airport detection in remote sensing images (RSIs). In recent years, Deep CNNs have gained much attention with numerous applications having been undertaken in the area of computer vision. Researchers generally approach airport detection as a pattern recognition problem, in which first various distinctive features are extracted, and then a classifier is adopted to detect airports. CNNs not o...
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.