This research suggests a robust and systematic way for Arabic Sentiment Analysis using a vast dataset of 66,666 text reviews. One of the main advantages of this study is that the dataset was perfectly balanced (33,333 positive samples and 33,333 negative samples). In machine learning, this 50/50 split is important because it eliminates class bias and enables the predictive model to treat both sentiment classes equally. As shown in the values of the metrics — overall accuracy, weighted precision, weighted recall, and F1 score — there is great similarity among them, indicating a stable and reliable assessment of the model's real potential throughout the Arabic dataset. Based on data profile, the average word count per review is 42.37 words, which is sufficient for classification of text using linguistic context. A high-performance machine learning pipeline was employed to process this data. The feature extraction step uses TF-IDF Vectorization (Term Frequency-Inverse Document Frequency). By this method, the model is able to identify not only individual words but also word pairs (bigrams), which help it understand the subtleties of the Arabic language. The classification algorithm used is the Linear Support Vector Classifier (LinearSVC), which is best suited to process high-dimensional text data and achieves optimal separation of positive and negative sentiments with maximum margin. A key step of the method is Arabic-specific preprocessing, which included extensive text cleaning, punctuation removal, normalization of characters to make different forms of the same letter identical, and stop-word filtering. These steps caused a great reduction of noise and helped the model to concentrate on sentiment-carrying words. The final experimental outcomes show a high degree of accuracy at 84.73%. This study demonstrates that the combination of TF-IDF and LinearSVC, along with the use of a balanced dataset and improved preprocessing, is an extremely successful solution for large-scale Arabic sentiment classification tasks.
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