Çok Etiketli Duygu Sınıflandırmasında Kelime Gömme Yöntemlerinin Derin Öğrenme Modelleri ile Kapsamlı Bir Değerlendirmesi

Abstract

Bu çalışmada, duygu analizi alanında kullanılan farklı kelime gömme yöntemlerinin derin öğrenme modellerinin performansı üzerindeki etkileri incelenmiştir. Google tarafından geliştirilen GoEmotions veri kümesi kullanılarak çok etiketli metinler üzerinde duygu sınıflandırması gerçekleştirilmiştir. Deneysel süreçte GloVe, FastText ve Word2Vec olmak üzere üç farklı kelime gömme yöntemi uygulanmış ve elde edilen vektör temsilleri çeşitli derin öğrenme mimarilerine (CNN, RNN, LSTM, GRU) girdi olarak verilmiştir. Ayrıca, bu mimarilere dikkat (attention) mekanizması entegre edilerek, dikkat katmanının model performansı üzerindeki etkisi analiz edilmiştir. Elde edilen bulgular, kelime gömme yöntemlerinin duygu sınıflandırma modellerinin başarımı üzerinde anlamlı farklılıklar yarattığını göstermektedir. Özellikle, CNN+Attention mimarisi ile GloVe gömme yönteminin kullanıldığı senaryoda en yüksek kesinlik değeri (%71.60) elde edilmiştir. Öte yandan, en dengeli ve etkili performans, BiGRU+Attention mimarisi ile GloVe yönteminin birlikte kullanıldığı durumda %56.11’lik F1 skoru ile gözlemlenmiştir. Literatür incelemesi sonucunda, GoEmotions veri kümesi gibi çok sınıflı bir yapıya sahip veri seti üzerinde üç farklı gömme yönteminin farklı derin öğrenme mimarileri ile karşılaştırmalı olarak değerlendirildiği benzer bir çalışmaya rastlanmamıştır. Bu sonuçlar, duygu analizi uygulamalarında uygun kelime gömme yönteminin seçiminde dikkatli olunması gerektiğini ortaya koymakta ve kelime temsil teknikleri ile derin öğrenme mimarilerinin entegrasyonuna yönelik özgün katkılar sunmaktadır.

Keywords

• Doğal Dil İşleme
• Metin Sınıflandırma
• Çok Etiketli Duygu Analizi
• Kelime Gömme
• Derin Öğrenme

A Comprehensive Evaluation of Word Embedding Methods with Deep Learning Models for Multi-Label Emotion Classification

Abstract

This study investigates the effects of different word embedding methods on the performance of deep learning models in the field of emotion analysis. Using the GoEmotions dataset developed by Google, emotion classification was performed on multi-label texts. In the experimental procedure, three distinct word embedding methods — GloVe, FastText, and Word2Vec — were applied, and the resulting vector representations were fed into various deep learning architectures (CNN, RNN, LSTM, GRU). In addition, an attention mechanism was integrated into these architectures to analyze the impact of attention layers on model performance. The findings indicate that word embedding methods produce statistically meaningful differences in the success of emotion classification models. In particular, the highest precision was achieved (71.60%) in the scenario combining the CNN+Attention architecture with GloVe embeddings. On the other hand, the most balanced and effective overall performance was observed for the BiGRU+Attention architecture paired with GloVe, yielding an F1 score of 56.11%. A literature review revealed no prior study that comparatively evaluates three different embedding methods across multiple deep learning architectures on a multi-label dataset such as GoEmotions. These results underscore the need for careful selection of appropriate word embedding techniques in emotion analysis applications and provide original contributions to the integration of word representation techniques with deep learning architectures.

Keywords

• Natural Language Processing
• Text Classification
• Multi-Label Emotion Analysis
• Word Embedding
• Deep Learning

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