Customer Complaint Classification with Large Language Models
Küçük Resim Yok
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Automatic classification of customer complaints is of great importance for companies to detect customer problems early, optimize service processes and increase customer satisfaction. Customer complaint data provided as textual data consists of very insightful information in terms of addressing the problems encountered. In this study, deep learning-based approaches (LSTM, CNN), transformer-based models (BERT, RoBERTa) and large language models (GPT-2, Llama3.1-8B) are compared using two customer complaint datasets with different class distributions. The results show that LLMs achieve higher accuracy and F1 scores in both training and test phases. In particular, the fine-tuning process applied to the large language models enabled them to be tailored to the problem and contributed to the higher performance of LLMs compared to other models. © 2025 IEEE.
Açıklama
2nd International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2025 -- 7 August 2025 through 9 August 2025 -- Antalya -- 213104
Anahtar Kelimeler
Bert, Customer complaint, GPT, Llama, LLM, RoBERTa
Kaynak
International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2025
WoS Q Değeri
Scopus Q Değeri
N/A
