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 the extracted terms from semantic user model, Word Embeddings-Based-Sentiment Analysis (WEBSA) must recommend the learning materials with highest rating to the learners properly. Variant models of (WEBSA) are proposed relying on Natural Language Processing (NLP) to generate effective vocabulary representations along with the use of qualitative customized Convolutional Neural Network (CNN) for sentiment multi-classification tasks. To validate the language model, two datasets are used, a tailored dataset that has been created by scraping reviews of different e-learning resources, and a public dataset. From the experimental results, it has been found that the lowest error rate is achieved with our customized dataset, where the model named CNN-Specific-Task-CBOWBSA outperforms than others with 89.26% accuracy.

E-learning proves its importance in the diverse educational levels over traditional education. An adaptive e-learning system needs to deduce the learner model for adding personalization to instructional websites. The learner model is the perception repository about the e-content user, which can be inferred implicitly by employing meaningful semantic analysis of the text. In this research, a novel methodology is proposed to conceptually deduce the semantic learner model for personalized e-learning recommendations. Firstly, Conceptual Learner Model (CLM) is developed based on the learner’s behavior and context-based text semantic representation by exploiting concepts from the ConceptNet knowledge base, with a significant association of patterns and rules. Then, Expanded Contextual Learner Model (ECLM) is developed by exploring the latent semantics in graphs to add concepts with the common-sense meanings that exceeded the named entities. The learner’s knowledge graph is defined based on contextually associated concepts. Semantic relations in ConceptNet are exploited to extend learner models. The Normalized Particle Swarm Optimization (NPSO) algorithm is used to learn the importance of the relation types between the concepts. Thus, CLM and ECLM each are represented as a vector of weighted concepts in which updating is obtained automatically. The proposed recommendation system incorporates dynamic learner models to predict an appropriate e-content with the highest ranking, matching the true needs of a particular learner. Our simulation results show that the performance of ECLM is better Mean Reciprocal Rank (MRR) value 0.780 than other existing methods.