IronyTR: Irony detection in turkish informal texts

ÖZTÜRK A. U., Cemek Y., KARAGÖZ P.

International Journal of Intelligent Information Technologies, cilt.17, sa.4, 2021 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 17 Sayı: 4
  • Basım Tarihi: 2021
  • Doi Numarası: 10.4018/ijiit.289965
  • Dergi Adı: International Journal of Intelligent Information Technologies
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Library and Information Science Abstracts, Metadex, Civil Engineering Abstracts
  • Anahtar Kelimeler: Classification, Deep Neural Networks, Feature Extraction, Graph Model, Information Extraction, Irony, Sentiment Analysis, Supervised Learning, Text Mining
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

© 2021 IGI Global. All rights reserved.Irony, which is a way of expression through the use of the opposite, commonly occurs in daily social media posts. Hence, automatic detection of irony is essential to understand the semantics of informal texts more accurately. The literature has several sentiment analysis studies on Turkish texts, but those focusing on irony detection are very few. This paper investigates the effectiveness of a rich set of supervised learning methods varying from traditional to deep neural solutions on Turkish texts. Traditional irony detection methods such as support vector machine (SVM) and tree-based binary classifiers are analyzed on Turkish informal texts. Furthermore, such methods are extended by polarity-based information and graph-based similarity scores as features. Additionally, neural architecture-based solutions including BERT and various LSTM network models are adapted for the problem. Irony detection performance of all the methods are comparatively analyzed on a data set collected within this study, which is larger than the previously used irony detection data sets in Turkish.