The paper provides a comprehensive overview of text classification algorithms, covering various aspects such as feature extraction, dimensionality reduction, classification techniques, and evaluation methods. It discusses the importance of each step in the text classification pipeline and highlights the challenges and limitations of different techniques. The paper also explores advanced word embedding methods like Word2Vec, GloVe, and FastText, which capture the semantic and syntactic relationships between words. Additionally, it reviews dimensionality reduction techniques such as PCA, LDA, NMF, random projection, autoencoders, and t-SNE, and their applications in text classification. The limitations of these techniques are discussed, along with their suitability for different types of text data and real-world problems. The paper concludes by comparing the strengths and weaknesses of various methods and providing insights into the best practices for choosing the appropriate technique for specific applications.The paper provides a comprehensive overview of text classification algorithms, covering various aspects such as feature extraction, dimensionality reduction, classification techniques, and evaluation methods. It discusses the importance of each step in the text classification pipeline and highlights the challenges and limitations of different techniques. The paper also explores advanced word embedding methods like Word2Vec, GloVe, and FastText, which capture the semantic and syntactic relationships between words. Additionally, it reviews dimensionality reduction techniques such as PCA, LDA, NMF, random projection, autoencoders, and t-SNE, and their applications in text classification. The limitations of these techniques are discussed, along with their suitability for different types of text data and real-world problems. The paper concludes by comparing the strengths and weaknesses of various methods and providing insights into the best practices for choosing the appropriate technique for specific applications.