Sentiment Analysis
Arabic Natural Language Processing (NLP)
TF-IDF
LinearSVC
Machine
Learning
Twitter Data.
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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 42.3
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