This paper presents a comprehensive survey of sentiment analysis, focusing on the application of machine learning techniques such as Support Vector Machine (SVM) and Random Forest. The research covers preprocessing techniques, feature extraction, model training, evaluation, and the challenges encountered in sentiment analysis. The study evaluates the accuracy of SVM and Random Forest algorithms in sentiment classification tasks. SVM achieved an accuracy of 0.80394, slightly higher than Random Forest's 0.78564. Both algorithms demonstrated strong performance, with SVM being more suitable for tasks where accuracy is a primary concern. The paper also discusses the strengths and limitations of each algorithm, emphasizing the importance of considering the specific characteristics of the problem when choosing the best algorithm. The findings contribute to a deeper understanding of sentiment analysis and provide insights into the effectiveness of machine learning approaches in this domain.This paper presents a comprehensive survey of sentiment analysis, focusing on the application of machine learning techniques such as Support Vector Machine (SVM) and Random Forest. The research covers preprocessing techniques, feature extraction, model training, evaluation, and the challenges encountered in sentiment analysis. The study evaluates the accuracy of SVM and Random Forest algorithms in sentiment classification tasks. SVM achieved an accuracy of 0.80394, slightly higher than Random Forest's 0.78564. Both algorithms demonstrated strong performance, with SVM being more suitable for tasks where accuracy is a primary concern. The paper also discusses the strengths and limitations of each algorithm, emphasizing the importance of considering the specific characteristics of the problem when choosing the best algorithm. The findings contribute to a deeper understanding of sentiment analysis and provide insights into the effectiveness of machine learning approaches in this domain.