Sentiment analysis, also known as opinion mining, identifies the opinion behind a series of words. Sentiment analysis is used to understand better the perception, thoughts, and feelings conveyed in a textual expression. In this study, sentiment analysis was made on the movie reviews collected from the Turkish movie site beyazperde.com. The proposed method is based on the pre-trained BERTurk model. In the first experiment, deep representations were extracted from the penultimate transformer layer of the BERTurk model and given as input to the Support Vector Machines (SVM). In the second experiment, the classification was carried out by fine-tuning on BERTurk. In the last experiment, deep representations were extracted from the fine-tuned BERTurk model as in the first experiment and the classification with SVM was completed. Experiments have shown that fine-tuned BERTurk representations reach the highest accuracy with a rate of 0.984. While the representations obtained at the end of the fine-tuning process caused an increase of about 10% in the accuracy rate, the combination of the representations obtained from BERTurk with the SVM instead of using BERTurk directly in the classification resulted in an accuracy increase of about 5%.