A Comparative Analysis of Transformer Architectures for Sentiment and Emotion Classification

Öz

This paper is a comparison of four Transformer model (BERT, ALBERT, T5, and XLNet) in the case of sentiment analysis of tasks based on data from social media. Used were two data sets: the X (Twitter) data set with tweets of messages related to games and the Emotion data set labeled in the anger, joy, and fear categories. Trained was the model in the same settings of preprocessing, training, as well as in the test settings for 3, 5, 7, and 10 epochs. Presented were results that indicated that the accuracy increased with the higher number of epochs. The maximum accuracies occurred in the case of the BERT model—88.63% in the case of the X data set as well as in the case of the Emotion data set, 97.05%. XLNet established great potential for long-range dependencies, and ALBERT obtained balanced performance due to lightweight architecture. On the contrary, performance of T5 was less in comparison to others. Generally, it could be inferred that Transformer architecture is superior to traditional machine learning technique in the context of sentiment analysis due to higher accuracy and better contextual understanding.

Anahtar Kelimeler

• Albert
• Natural Language Processing
• Sentiment Classification
• Transformer Models

Duygu ve Duygusal Durum Sınıflandırması İçin Transformer Mimarilerinin Karşılaştırmalı Analizi

Öz

Bu makale, sosyal medya verilerine dayalı duygu analizi görevlerinde dört farklı Transformer modelinin (BERT, ALBERT, T5 ve XLNet) karşılaştırılmasını içermektedir. Çalışmada iki veri seti kullanılmıştır: oyunlarla ilgili tweet’leri içeren X (Twitter) veri seti ve öfke, sevinç ve korku kategorilerinde etiketlenmiş Emotion veri seti. Modeller; ön işleme, eğitim ve test ayarlarında aynı koşullarda, 3, 5, 7 ve 10 epoch boyunca eğitilmiştir. Elde edilen sonuçlar, epoch sayısı arttıkça doğruluğun yükseldiğini göstermektedir. En yüksek doğruluk oranları BERT modeli için elde edilmiştir: X veri setinde %88,63, Emotion veri setinde ise %97,05. XLNet uzun menzilli bağımlılıkları yakalama konusunda yüksek potansiyel sergilerken, ALBERT hafif mimarisi sayesinde dengeli bir performans göstermiştir. Buna karşın T5 modeli, diğer modellere kıyasla daha düşük performans sergilemiştir.

Anahtar Kelimeler

• Albert
• Doğal dil işleme
• Duygu Sınıflandırması
• Transformer Modelleri

Kaynakça

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