Music has been an integral part of humanity throughout history. People have conveyed their emotional expressions through music, and musical styles have evolved alongside communities. Despite the diversity of styles, music has always existed within an emotional context. Therefore, measuring the emotional expressions conveyed by music has given rise to a broad field of study encompassing art, science, history, and sociology. Additionally, with the proliferation of electronic music platforms, the ability to automatically identify the emotional genres of music has become a prominent feature sought after by end users. In this context, while numerous studies have been conducted in various languages, there is a scarcity of research specifically tailored to the Turkish language. For successful execution of processes that can be automated through machine learning, several factors need to be considered: the proper selection of data preprocessing methods, determination of the structure and complexity of the model to be trained, accurate selection of training and testing data, and more. Optimal performance cannot be achieved solely through the correct choice of a model, as flawed data preprocessing can hinder results, and conversely, accurate data preprocessing cannot compensate for a faulty model. This article aims to enhance the performance of a rare music emotion recognition study conducted in the Turkish language by constructing a "problem-specific network model." To achieve this goal, data subjected to various normalization techniques were analyzed using Convolutional Neural Network (CNN) models of different dimensions and complexities. The achievements were compared with two different classifiers to establish a reference point in comparison with previous studies. At the end of the study, it was observed that for data subjected to MinMax normalization, a success rate of 86.67% was achieved with the Softmax classifier and 80% with the SVM classifier. Similarly, with Z-Score normalization, success rates of 84.17% and 81.67% were obtained, respectively. These values are higher than the highest achievement value of 74.2% obtained for the same data group in the reference study. Furthermore, it is believed that applying the additional performance-enhancing procedures used in the reference study to the models in this study would lead to even higher achievements.