Real-Time Text Classification Algorithm Based On Combined Machine Learning Models

Web text categorization is a prominent area of research and a vital technology in the fields of Web information retrieval and data mining. A great deal of attention and quick progress in this field has occurred in recent years. One sort of deep learning model that has been more useful for extracting important characteristics from textual input while reducing model complexity is the convolutional neural network (CNN). In the meanwhile, a time-tested machine learning method known as support vector machine (SVM) has a stellar reputation for reliability and accuracy. By merging an improved CNN with SVM, this research suggests a new method for Web text categorization. Text characteristics are extracted using the CNN model, which has a five-layer network structure. These features are then used for classification as well as forecasting using SVM. This method presents a new approach to Web text categorization using enhanced models by combining the capabilities of convolutional neural networks (CNNs) and support vector machines (SVMs). Applying this method to datasets with mixed text yields outstanding results, all things considered.