Trends in natural language processing for text classification: A comprehensive survey

Text classification has become a cornerstone in natural language processing (NLP), facilitating a wide range of applications such as sentiment analysis, spam detection, and hate speech moderation. This comprehensive survey explores the historical evolution of text classification methods, beginning with statistical techniques like Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF), progressing through classical machine learning algorithms such as Support Vector Machines (SVMs) and Naive Bayes, and culminating in the transformative impact of deep learning models like RNNs, CNNs, and transformers. Special emphasis is placed on emerging trends, including zero-shot learning, multilingual models, explainable AI, and resource-efficient architectures like TinyBERT. The paper also examines the challenges and limitations of text classification, such as data bias, ethical concerns, and computational resource demands, while highlighting opportunities for future advancements in real-time processing, cross-domain generalization, and hybrid symbolic-neural systems. The insights presented aim to guide researchers and practitioners in leveraging state-of-the-art technologies to address real-world challenges in text classification effectively.