The views and inputs expressed by the community have always been a crucial and valuable resource for various enterprises. The advent of widespread community media has provided an exceptional opportunity for studying and assessing diverse fields, replacing the peculiar, laborious, and inaccurate approaches that companies used to rely on. This particular type of analysis falls under the subclass of sentence analysis. Sentiment analysis, a broad term, refers to the process of effectively classifying usergenerated content into specific polarities. To perform sentiment identification and analysis, a range of tools and techniques are available, including supervised machine learning techniques that classify the target group after training on data. Hybrid instruments, combining machine learning and lexicon-based algorithms, classify content based on annotated dictionaries. In this study, we employed the Support Vector Machine (SVM) algorithm with Weka, an open-source software, to analyze sentiments. Two precategorized datasets of tweets were utilized. The performance of the SVM algorithm was assessed using analytical metrics.