Doğal Dil İşlemede Yerel Dil Zenginliği: Azbert ile Azerbaycan Türkçesi Metinlerinin Anlamsal Analizi

Year 2025, Volume: 1 Issue: 2, 27 - 57

Orkhan Valiev , Gülsüm Kayabaşı Koru

Abstract

Azerbaycan Türkçesinde metin sınıflandırma ve duygu analizi görevlerini yerine getirmek amacıyla tasarlanan BERT tabanlı modelin amacı, metinleri olumlu, olumsuz veya tarafsız olarak sınıflandırmaktır. Dönüştürücü mimarisine dayanan modelin eğitimi, daha önce eğitilmiş olan BERT modeli üzerinde ince ayar yapılarak gerçekleştirilir. Bu eğitimde dilin morfolojik zenginliği ve yerel ifadeleri dikkate alınmaktadır. Modelin etkinliğini değerlendirmek için doğruluk, kesinlik, geri çağırma ve F1 puanı gibi performans ölçütleri kullanılmıştır. Değerlendirme sonuçları, modelin %96 doğruluk ve %95 F1 skoru elde ettiğini ortaya koymuştur. Özellikle pozitif ve negatif sınıflarda yüksek bir doğruluk seviyesine ulaşılmıştır; ancak yerel dil ve yapısal unsurların kullanımı nedeniyle bazı sınıflandırma hataları meydana gelmiştir. Morfolojik ve semantik dil unsurlarının dikkate alınmasının önemli bir husus olduğunu gösteren bulgular, duygu analizine önemli bir katkı sağlamaktadır. Daha büyük veri kümeleri ve yerel dile özgü modellerle yapılacak daha fazla araştırmanın performansı daha da artırma olasılığı yüksektir.

Keywords

Azerbaycan Türkçesi , Duygu Analizi , Metin Sınıflandırma , BERT , Doğal Dil İşleme

Natural Language Processing in Azerbaijani: Text Analysis And Artificial Intelligence Applications

Abstract

Classifying texts as either positive, negative, or neutral is the goal of the BERT-based model that was designed for the purpose of performing text classification and sentiment analysis tasks in Azerbaijani Turkish. The training of the model, which is based on transformer architecture, is accomplished by performing fine-tuning on the BERT model that has already been trained. This training takes into consideration the morphological richness and local expressions of the language. Performance metrics such as accuracy, precision, recall, and F1 score were used in order to assess the effectiveness of the model. The results of the evaluation revealed that the model achieved a 96% accuracy and a 95% F1 score. We were able to attain a high level of accuracy, particularly in the positive and negative classes; nevertheless, there were some classification mistakes that occurred due to the use of local language and structural elements. A significant addition to sentiment analysis is shown by the findings, which indicate that taking into account morphological and semantic language aspects is an essential aspect. There is a high probability that more research with bigger datasets and models that are particular to the local language can further enhance performance.

Keywords

Azerbaijani Turkish , Sentiment Analysis , Text Classification , BERT , Natural Language Processing

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