Türkçe nefret söylemi tespitinde veri dengeleme tekniklerinin performans karşılaştırması

Öz

Sosyal medya platformlarında artan nefret söylemleri, psikolojik rahatsızlıklara, derin ve olumsuz etkilere neden olmaktadır. Nefret söylemlerini tespit etmek için otomatik dil sınıflandırma modellerine ihtiyaç vardır. Nefret söylemleri için dil modelleri test edilirken, bir veri sınıfının diğerinden çok daha sık temsil edildiği dengesiz veri kümeleri dil verilerinde sorun teşkil edebilir. Veri kümesi dengesiz dağılıma sahip olduğunda, sınıflandırıcı çoğunluk sınıfına yönelik önyargılı olabilir ve azınlık sınıfında iyi performans göstermeyebilir. Bu, yanlış veya güvenilmez sınıflandırma sonuçlarına yol açabilir. Bu sorunu çözmek için veri kümesi sınıflandırılmadan önce oversampling veya undersampling gibi veri düzeyi dengeleme yöntemleri ile veri sınıfları dengelenir. Bu çalışmada, veri düzeyi dengeleme yöntemleri kullanılarak nefret söylemini tespit eden başarılı bir sınıflandırma modeli kombinasyonu elde etmek amaçlanmaktadır. Bu amaçla, Instagram'dan elde edilmiş dengesiz etiket dağılımına sahip Abusive Turkish Comments (ATC) veri kümesine sekiz veri düzeyinde (rastgele oversampling, Synthetic Minority Oversampling Technique (SMOTE), Kmeans SMOTE, Localized Random Affine Shadow Sample (LoRAS), Textbased Generative Adversarial Network (TextGAN), Nearmiss, Tomek Links ve Clustering-based) dengeleme yöntemi uygulanarak kapsamlı bir çalışma yapılmıştır. Veri düzeyi dengeleme yöntemlerinin sınıflandırma performansları Basic Machine Learning (BML) ve Convolutional Neural Network (CNN) yöntemleriyle değerlendirilmiştir. TextGAN veri düzeyi dengeleme yöntemine dayalı CBoW+CNN modelinin ve Skip-gram CNN modelinin 0,972 Makro Ortalamalı F1 puanı ile en iyi sınıflandırma performansını sergilediği görülmüştür.

Anahtar Kelimeler

• Veri dengeleme
• Sosyal medya
• Makine öğrenmesi
• Derin öğrenme
• Doğal dil işleme
• nefret söylemi

Performance comparison of data balancing techniques on hate speech detection in Turkish

Öz

Increasing hate speech on social media platforms causes psychological disorders and deep and negative effects. Automatic language classification models are needed to detect hate speech. When testing language models for hate speech, imbalanced datasets where one data class is represented much more frequently than the other can be a problem in language datasets. When the dataset is imbalanced, the classifier may be biased towards the majority class and may not perform well in the minority class. This can lead to incorrect or unreliable classification results. To solve this problem, data level balancing methods such as oversampling or undersampling are used to balance the class distribution before classifying the dataset. This study, it is aimed to achieve a successful classification model combination that detects hate speech by using data-level balancing methods. For this, a comprehensive study was carried out by applying the balancing method at eight data levels (random oversampling, Synthetic Minority Oversampling Technique (SMOTE), K-means SMOTE, Localized Random Affine Shadow Sample (LoRAS), Text-based Generative AdversarialNetwork (TextGAN), Nearmiss, Tomek Links ve Clustering-based) to the Abusive Turkish Comments (ATC) dataset, which has an imbalanced distribution of labels, obtained from Instagram. Classification performances of data level balancing methods were evaluated with Basic Machine Learning (BML) and Convolutional Neural Network (CNN) methods. It has been observed that the CBoW+CNN model based on the TextGAN data-level balancing method, as well as the Skip-gram CNN model, exhibited the best classification performance with a MacroAveraged F1 score of 0.972.

Anahtar Kelimeler

• Data balancing
• Social media
• Machine learning
• Deep learning
• Natural language processing
• Hate speech

Kaynakça

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