Unlocking Text Classification Through Supervised Learning Techniques

Abstract

Text classification, a fundamental task in Natural Language Processing (NLP), organises unstructured textual data into predefined categories using supervised learning techniques. This study explores traditional algorithms like Naïve Bayes, Support Vector Machines (SVM), and K-Nearest Neighbours (KNN), alongside advanced deep learning models such as Transformers (e.g., BERT). These methods enhance classification accuracy by leveraging contextual and semantic nuances. The paper addresses challenges like high-dimensional feature spaces, class imbalance, and data scarcity, proposing solutions like feature engineering, Synthetic Minority Oversampling Technique (SMOTE), and transfer learning. Future advancements focus on explainable AI, multilingual capabilities, zero-shot learning, and ethical AI practices to improve scalability, adaptability, and transparency. By integrating traditional and cutting-edge methods, text classification continues to evolve, unlocking applications across domains like sentiment analysis, spam detection, and customer feedback categorisation, driving innovations in NLP and data-driven decision-making.