Review On Text Classification Using Improved Deep Learning Models

Modern deep learning models have exhibited better performance than conventional machine learning methods across a range of text classification tasks, encompassing natural language inference, sentiment analysis, news categorization, and question answering. The application of text classification in natural language processing extends to tasks like spam detection, news categorization, information retrieval, sentiment analysis, and intent assessment. Traditional text classifiers utilizing machine learning techniques encounter challenges such as sparse data, dimensionality issues, and limited generalization capabilities. In contrast, classifiers based on deep learning effectively tackle these limitations, eliminating the need for intricate feature extraction processes, and providing improved learning capabilities along with enhanced prediction accuracy. One notable instance of Convolutional neural networks are used in this way (CNNs). This paper delineates the text classification process, emphasizing the application of deep learning methodologies for text classification.