E-Learning system is one of the most crucial system in today’s education. In order to fulfill the goal of E-Learning, learning center departments of educational institutes need to know what the user needs and the only way to communicate with them is through feedback. However, it is a hard and time-consuming task to extract value from a large amount of feedback. This paper aims to implement and evaluate various machine learning techniques to be able to classify E-Learning feedback text to its categories via a multiclass classification approach. Furthermore, this paper uses FastText, Keras Embedding Layer, and TF-IDF to extract features along with the use of various data resampling techniques, such as Random Oversampling and SMOTE, in order to deal with imbalanced dataset problem.