IMPROVING E-GOVERNMENT THROUGH SENTIMENT ANALYSIS IN THE CONTEXT OF DEVELOPING COUNTRIES: A MAURITANIA CASE STUDY

New technologies have given rise to online platforms such as Facebook and X (formerly Twitter), which have become de facto channels of communication, enabling people to express their opinions in writing. Analyzing the user-generated data makes it possible to identify opinions and sentiments on specific topics, thereby providing valuable insights for evidence-based decision-making. Therefore, this study focuses on the HASSANIYA dialect, a low-resource variety of Arabic, and proposes a sentiment classification system based on Natural Language Processing (NLP) to support decision-making. In the first step, A dedicated dataset was collected from Facebook, preprocessed using a combination of a Hassaniya-specific stemmer and an Arabic stemmer. Then, different configurations of feature extraction (n-grams) and weighting (TF-IDF) were applied to construct effective feature representations and determine the best classification model. Support Vector Machines (SVM), Logistic Regression (LR), and Random Forest (RF) were used to classify the HASSANIYA dataset. The proposed approach was designed to analyze comments written in both Modern Standard Arabic (MSA) and the HASSANIYA dialect on Facebook and Twitter. The results show that SVM achieved the best performance and was successfully validated through a real case study involving the classification of comments from an X account (Twitter). Finally, this study introduces a prototype dashboard for visualizing sentiment trends, offering a practical tool to support e-government strategies in Mauritania.