IMPROVING ARABIC E-LEARNING USER EXPERIENCE THROUGH SENTIMENT ANALYSIS AND COLLABORATIVE FILTERING MODELS
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Date
2025-07-26
Authors
Yamin, Aya Said
Journal Title
Journal ISSN
Volume Title
Publisher
An-Najah National University
Abstract
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.