Sentiment analysis, a crucial component of natural language processing (NLP), aims to extract subjective information from text. This article presents a comprehensive review of feature extraction, reduction, and traditional classification methods in sentiment analysis. The study explores various approaches, including machine learning algorithms, lexicon-based methods, and deep learning models. It discusses the challenges associated with sentiment analysis and highlights the strengths and weaknesses of each technique. The review includes a literature survey of relevant research papers, showcasing innovative methodologies and their application in different domains such as fake news detection, social media sentiment analysis, and public health analysis. The findings emphasize the significance of accurate sentiment analysis in understanding user opinions, public perceptions, and effective communication. The article concludes by discussing the sentiment analysis process and key techniques, including dataset acquisition, pre-processing, feature extraction and reduction methods like Bag of Words (BOW) and TF-IDF, and the use of principal component analysis (PCA) for dimensionality reduction. Further, explores several machine learning algorithms commonly employed in sentiment analysis. These algorithms include Support Vector Machines (SVM), Naive Bayes, Random Forest, and Logistic Regression. SVM is known for its ability to handle highdimensional feature spaces and non-linear data. Naive Bayes is a probabilistic classifier that assumes feature independence and is efficient for text classification tasks. Random Forest is an ensemble method that combines multiple decision trees to improve classification accuracy. Logistic Regression is a linear model widely used for binary classification tasks. The article discusses the strengths and limitations of each algorithm and highlights their applicability in sentiment analysis tasks. It also presents comparative studies to evaluate the performance of these machine learning algorithms in sentiment classification.