Performance of Transformer-Based Methods on Restaurant Reviews Analysis

Authors : Mücahit Karaduman, Muhammed Bedir Baydemir, Muhammed Yıldırım

Pages : 351-362

Doi:10.62520/fujece.1632266

View : 53 | Download : 32

Publication Date : 2025-06-26

Article Type : Research Paper

Abstract :Sentiment analysis provides important data in various areas, from customer feedback to social media posts, by determining the text\\\'s emotional tones. In this study, sentiment analysis was performed using restaurant reviews with a transformer-based model. The attention mechanism underlying these models dynamically learns the contextual relationships of words in the text and better captures the meaning of the language. The model was trained and tested using a dataset from a vast information source. First, tokenization and padding operations of the dataset were performed; then, the model was trained, and test results were obtained. The training accuracy of the model was calculated as 90.81% and the test accuracy as 85.79%. When other performance metrics were also considered, the model, which achieved high success for negative and positive classes, showed lower success for the neutral class. In terms of general evaluation, it is seen that the model exhibited good performance when the accuracy rate was taken into account. This shows that transformer-based approaches are suitable for natural language processing and usability in this area.
Keywords : Duygu analizi, Transformatör, NLP, Metin sınıflandırma

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