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Enhancing virtual learning platforms need to adapt new intelligent mechanisms so that long-term learner experience can be improved. Sentiment Analysis gives us perception on how a specific scientific material is suitable to be recommended to the learner. It depends on the feedback of a similar learner taking many factors under consideration such as preference, knowledge level, and learning pattern. In this work, a hybrid e-learning recommendation system is proposed based on individualization and Sentiment Analysis. A new approach is provided for modelling the semantic user model based on the generated semantic matrix to capture the learner's preferences based on their selections of interest. The extracted semantic matrix is used for text representation by utilizing ConceptNet knowledge base which relies on contextual graph and expanded terms to represent the correlation among terms and materials. On …