The growing popularity of social media sites has generated a massive amount of data that attracted researchers, decision-makers, and companies to investigate people’s opinions and thoughts in various fields. Sentiment analysis is considered an emerging topic recently. Decision-makers, companies, and service providers as well-considered sentiment analysis as a valuable tool for improvement. This research paper aims to obtain a dataset of tweets and apply different machine learning algorithms to analyze and classify texts. This research paper explored text classification accuracy while using different classifiers for classifying balanced and unbalanced datasets. It was found that the performance of different classifiers varied depending on the size of the dataset. The results also revealed that the Naive Byes and ID3 gave a better accuracy level than other classifiers, and the performance was better with the balanced datasets. The different classifiers (K-NN, Decision Tree, Random Forest, and Random Tree) gave a better performance with the unbalanced datasets.
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