Sentiment analysis of comments on courses on massive online course platforms using text mining

In our age, things that are easily accessible, less costly, and have no time and space restrictions attract more attention. Considering the possibilities that people have recently and the development of technology, this change in people's interest has also been reflected in education. People now want to access content that they can choose from wherever they want, whenever they want. As a result of these requests, Massive Open Online Course (MOOC) platforms began to emerge. There are many paid or free courses on these platforms. Before enrolling in these courses, many people register based on the comments made and the score given to the course. However, it is not easy to decide about a course by reading all the reviews. In this study, comments made on courses on Udemy, one of the MOOC platforms, were used in order to evaluate the courses positively and negatively without the need for users to read the comments. On these comments, positive and negative evaluations were made about the courses using classical machine learning and deep learning. With BayesNet, J48 and OneR algorithms from classical machine learning, the most successful result was obtained from BayesNet algorithm with an accuracy of 91.576%. After applying Random, GloVe and Word2Vec word embeddings to the dataset, hybrid architectures of GRU and CNN-LSTM from deep learning models were applied and the most successful result was obtained from GRU architecture with an accuracy of 95.67% after using GloVe word embedding.