Facebook and Twitter, as two known social media become popular sources of big data that give the right to people to share and express their feedback about products, services, politicians, events, and every aspect of life in the form of short texts. The classification of sentiments could be automated through machine learning and enhanced using appropriate feature extraction methods. In this work, we collected the most recent tweets about (Biden, Benzema, Apple, and NASA) using Twitter-API and

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... igned sentiment scores using a rule-based lexicon approach; after pre-processing stage, each dataset is divided into 80% as a training set, and rest 20% as testing set. After that, the Distributed bag of words, Distributed memory mean, Distributed Memory Concatenation, and Term Frequency-Inverse Document Frequency models are used for feature extraction from pre-processed tweets. Depending on the Shark smell optimizer algorithm, the SVM technique was used to classify the extracted features. The SSO was used to tune and select the best value for SVM parameters to optimize the overall model performance. The results display that these optimizers have an essential impact on increasing the model accuracy. After optimization, the model accuracy reached 92.12%, while the highest accuracy without optimization was 88.69% for various feature extraction methods.