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This study presents a hybrid model that leverages the strengths of K-means clustering and Support Vector Machines (SVM) for classifying online product reviews. K-means is used to group reviews into clusters, reducing data complexity and improving feature extraction. Subsequently, SVM is employed to classify the clustered data into positive, negative, or neutral sentiments. The combined approach enhances classification accuracy, reduces computational cost, and effectively handles large datasets. Experimental results demonstrate that the proposed model outperforms traditional standalone classifiers in terms of precision, recall, and overall accuracy.