ÜRETKEN YAPAY ZEKA MODELLERİNİN TEKNİK ÖZELLİKLERİ VE KULLANICI GERİ BİLDİRİMLERİNİN ANALİZİ: CHATGPT ÖRNEĞİ

Yıl 2025, Cilt: 14 Sayı: 2, 744 - 770, 31.12.2025

Serkan Metin

https://doi.org/10.54282/inijoss.1763596

Öz

Bu çalışma, üretken yapay zekâ modellerinin teknik özelliklerini ve kullanıcı deneyimlerini bütüncül bir biçimde inceleyerek ChatGPT örneği üzerinden kapsamlı bir analiz sunmaktadır. Üretken yapay zekânın (ÜYZ) temel bileşenleri, transformer tabanlı büyük dil modelleri, GAN ve VAE gibi alternatif üretici mimariler kuramsal arka plan olarak ele alınmıştır. Bu bağlamda çalışmanın özgün katkısı, ÜYZ teknolojilerinin teknik altyapısı ile kullanıcı yorumlarından elde edilen duygu ve şikâyet analizini birlikte değerlendirmesidir. Veri kümesi Kaggle üzerinde yer alan 196.720 kullanıcı yorumundan oluşmakta olup, yorumlara ilişkin puanlar temel alınarak üç sınıflı (pozitif-nötr-negatif) duygu analizi yapılmıştır. TF-IDF, n-gram ve kelime frekans analizleri metinsel örüntüleri ortaya çıkarmış; ardından Karar Ağacı, Naive Bayes, KNN, SVM, MLP ve Rastgele Orman algoritmaları uygulanmıştır. Sonuçlara göre MLP ve Rastgele Orman modelleri %91 doğruluk ile en başarılı sınıflandırıcılar olmuştur. Ayrıca olumsuz yorumlara dayalı tematik şikâyet analizi gerçekleştirilmiş ve beş temel problem alanı hata mesajları, yükleme sorunları, fiyat eleştirileri, eksik cevaplar, görsel destek problemleri belirlenmiştir. Elde edilen bulgular, ChatGPT’nin genel olarak olumlu karşılandığını; ancak teknik kararlılık ve cevap tutarlılığı konularında geliştirmeye ihtiyaç duyulduğunu göstermektedir.

Anahtar Kelimeler

Üretken Yapay Zeka , Duygu Analizi , Veri Madenciliği , Makine Öğrenmesi , ChatGPT

TECHNICAL FEATURES OF GENERATIVE AI MODELS AND ANALYSIS OF USER FEEDBACK: THE CASE OF CHATGPT

Öz

This study provides a comprehensive analysis of the technical features of generative artificial intelligence (AI) models and user experiences, focusing on ChatGPT as a representative example. The theoretical background covers the core components of generative AI, including transformer-based large language models, GANs, and VAEs. The study’s main contribution lies in jointly evaluating the technical infrastructure of generative AI and the sentiment- and complaint-based insights extracted from user reviews. The dataset consists of 196,720 user comments collected from Kaggle, and a three-class sentiment analysis (positive-neutral-negative) was conducted using the rating scores assigned to each review. TF-IDF, n-gram, and word-frequency analyses were applied to identify textual patterns, followed by the implementation of Decision Tree, Naive Bayes, KNN, SVM, MLP, and Random Forest classifiers. The results indicate that MLP and Random Forest achieved the highest accuracy rates at 91%. Additionally, a thematic complaint analysis was carried out using only negative comments and five major problem categories were identified: error messages, loading issues, pricing concerns, incomplete responses, and limitations related to image-based functionalities. Findings suggest that while ChatGPT is generally perceived positively, improvements are needed in technical stability and response consistency.

Anahtar Kelimeler

Generative Artificial Intelligence , Sentiment Analysis , Data Mining , Machine Learning , ChatGPT

Kaynakça

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