E-Learning Course Recommendation: Combining Content Filtering and Sentiment Analysis

As e-learning becomes more popular, there is a greater demand for personalized and effective online course recommendation systems. In this study, we offer an e-learning online course recommendation system that analyses course reviews and organizes them based on their scores. Based on the content filtering, the algorithm then recommends the optimal route to the user. To do this, we collected data from several e-learning sites and analysed the reviews using natural language processing (NLP) techniques. The courses were then recommended using a content-based filtering technique based on their resemblance to the user's interests and past course selections. Sentiment analysis determines whether a given text contains emotions that are negative, positive, or neutral [15]. In terms of accuracy and user satisfaction, our trials reveal that our suggested system beats existing state-of-the-art recommendation systems. The system makes personalized suggestions based on the user's interests, making it simpler for them to pick the best course for them. As a result, it is essential to develop an online recommendation system that aids users in choosing the ideal online course for their needs and interests. [10]. Overall, our online course recommendation system for e-learning provides an effective and efficient method of recommending courses to users based on ratings and content screening