Artificial Intelligence

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https://hdl.handle.net/20.500.11888/20217

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Browsing Artificial Intelligence by Author "Yamin, Aya Said"

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    IMPROVING ARABIC E-LEARNING USER EXPERIENCE THROUGH SENTIMENT ANALYSIS AND COLLABORATIVE FILTERING MODELS
    (An-Najah National University, 2025-07-26) Yamin, Aya Said
    This study works to develop an enhanced recommendation system (RS) for the purpose of improving the user experiences in e-learning environments by achieving the integration between sentiment analysis (SA) and collaborative filtering (CF). It uses a publicly available English dataset from Coursera and translates it into Arabic using AWS translation services to be appropriate for the target language context. It targets Arabic-language course reviews, taking into consideration handling data sparsity and linguistic complexity challenges. A refined multilingual Bidirectional Encoder Representations from Transformers (BERT) model was used to produce sentiment labels, and support vector machine (SVM) classification with a combination of Term Frequency–Inverse Document Frequency (TF-IDF) and FastText features achieved good performance. And then the user-item interaction matrix was enriched with the sentiment scores that resulted from SA to make recommendations using Alternating Least Squares (ALS) for active users and using K-means clustering based on profiles, followed by hybrid K-Nearest Neighbors (KNN) and TF-IDF similarity on Arabic course names for cold users. The evaluation of the system is done by making a comparison before and after adding the effect of sentiment separately for active and cold users. The system specifically targets users or learners who plan to study Arabic courses on online platforms. The findings emphasize that this integration of sentiment information reduces the limitations related to cold-start user problems and also enhances personalization. For example, the sentiment-aware model achieved a reduction in RMSE of nearly 70% for active users and a significant improvement in success rate (from 19.57% to 93.27%) and recall (from 18.68% to 91.24%) for cold users. These improvements lead to considering it a practical, useful solution for e-learning platforms.