TY - JOUR T1 - The Effect of Various Text Representation Methods for Sentiment Analysis on Movie Review Data with Different Machine Learning Methods AU - Başarslan, Muhammet Sinan AU - Göç, Veysel PY - 2024 DA - December Y2 - 2024 DO - 10.29109/gujsc.1498509 JF - Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji JO - GUJS Part C PB - Gazi Üniversitesi WT - DergiPark SN - 2147-9526 SP - 893 EP - 901 VL - 12 IS - 4 LA - en AB - In this study, we explore the potential of machine learning (ML) models after different text representation methods on the balanced IMDB dataset, which is widely regarded as a gold standard in sentiment analysis, one of the Natural Language processing (NLP) tasks. On the open source IMDB movie reviews dataset, we first undertake data cleaning and text representation with data preprocessing steps. Then, we apply sentiment classification using different ML models. In order to evaluate the models, we used precision (P), recall (R), F1-score (F1), and area under curve (AUC), as well as receiver operating characteristic (ROC). It is worth noting that text feature extraction with Bidirectional Encoder Representations from Transformers (BERT) provided the highest performance in all models, with the SVM model offering particularly promising results. In this model, we observed the following results: ACC 0.9033, F1 0.9308, R 0.9015, R 0.9015, P 0.9072, AUC 0.9638, and ROC 0.96. These findings suggest that NLP techniques and, in particular, machine learning models that employ BERT may offer high levels of accuracy and reliability in text classification problems. It would be beneficial for future studies to validate these findings using BERT on different NLP tasks. This would help to evaluate the effectiveness and applicability of the models in practice. KW - Machine learning KW - movie review KW - sentiment analysis KW - text representation. CR - [1] A. 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