TEXT CLASSIFICATION BASED ON DEEP LEARNING

Text classification is a fundamental task in natural language processing (NLP), aiming to automatically assign predefined categories or labels to textual documents. Different standard machine learning methods have been used for text categorization tasks over the years, but they frequently have trouble with huge and complicated datasets. In this study, a unique deep learning-based method for classifying texts is presented. By recognizing complex patterns and semantic representations in textual input, deep learning models, in particular neural networks, have revolutionized the discipline of NLP. The key contribution of this research is the development of a deep learning architecture specifically designed for text classification. We explore various neural network architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based models, to capture both local and global dependencies in textual data. We investigate different methods for pre-processing, word embedding, and feature extraction to enhance the performance of our models.