Nowadays, many firms and companies are curious about what people think and want and they are working in this direction. For this reason, it is tried to learn the ideas and emotions of people in various ways. However, as it is impossible to process and analyze a large number of emotions and thoughts with human hands, emotion analysis gain more importance. The emotions and thoughts of the people are analyzed and acted according to these requests through the emotion analysis which is quite functional in social networks. The aim of this study is to realize the learning with the data sets obtained from the interpretations made to the social platforms of the determined brands and to transfer the subject of the emotion analysis to the researchers in the best way. The range of accuracy rates reached is wide because of the disadvantages such as not paying attention to the rules of writing on social media or other digital platforms. In our study, a accuracy rate of 70% was achieved. This demonstrates the usefulness of machine learning in interpretation classification and emotion analysis.