Satire identification in Turkish news articles based on ensemble of classifiers

Aytuğ ONAN; Mansur Alp TOÇOĞLU

doi:https://doi.org/10.3906/elk-1907-11

Makale detayları

Satire identification in Turkish news articles based on ensemble of classifiers

Yazarlar:

Aytuğ ONAN | Mansur Alp TOÇOĞLU

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2020 Vol.28 Issue 2, pp.1086-1106

Belge Tipi:

Makale

Yayın Tarihi:

2/2020

Belge Dili:

İngilizce

Document ID (Org No.):

2000733286

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

DOI:

https://doi.org/10.3906/elk-1907-11

Özet:

 

Social media and microblogging platforms generally contain elements of figurative and nonliteral language,  including satire. The identification of figurative language is a fundamental task for sentiment analysis. It will not be  possible to obtain sentiment analysis methods with high classification accuracy if elements of figurative language have  not been properly identified. Satirical text is a kind of figurative language, in which irony and humor have been utilized  to ridicule or criticize an event or entity. Satirical news is a pervasive issue on social media platforms, which can be  deceptive and harmful. This paper presents an ensemble scheme for satirical news identification in Turkish news articles.  In the presented scheme, linguistic and psychological feature sets have been utilized to extract the feature sets  i.e.  linguistic, psychological, personal, spoken categories, and punctuation . In the classification phase, accuracy rates of  five supervised learning algorithms  i.e. naive Bayes algorithm, logistic regression, support vector machines, random  forest, and k-nearest neighbor algorithm  with three widely utilized ensemble methods  i.e. AdaBoost, bagging, and  random subspace  have been considered. Based on the results, we concluded that the random forest algorithm yielded  the highest performance, with a classification accuracy of 96.92% for satire detection in Turkish. For deep learning-based  architectures, we have achieved classification accuracy of 97.72% with the recurrent neural network architecture with  attention mechanism.

Makale detayları

Satire identification in Turkish news articles based on ensemble of classifiers

Yazarlar: Aytuğ ONAN | Mansur Alp TOÇOĞLU

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2020 Vol.28 Issue 2, pp.1086-1106

Belge Tipi: Makale

Yayın Tarihi: 2/2020

Belge Dili: İngilizce

Document ID (Org No.): 2000733286

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

DOI: https://doi.org/10.3906/elk-1907-11

Sayı

Yazarlar:

Aytuğ ONAN | Mansur Alp TOÇOĞLU

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2020 Vol.28 Issue 2, pp.1086-1106

Belge Tipi:

Makale

Yayın Tarihi:

2/2020

Belge Dili:

İngilizce

Document ID (Org No.):

2000733286

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

DOI:

https://doi.org/10.3906/elk-1907-11