Classification of Online News Articles Using Optimized Deep Learning Models

Text mining has gained considerable importance in recent years, especially in the last few. Users can now get information from many different places, such as digital media, print media, electronic media, and others. Text categorization is the method by which researchers organize the huge quantities of unstructured data generated as a result of the broad availability of text in numerous formats. In the accessible corpus of study, these specialists have identified a multitude of techniques for transforming the dispersed text into a defined and organized volume. In this study, we explore the text classification process, classifiers, and multiple feature extraction approaches utilizing the word2vec model, the Google pre-trained model (Google News 300), the RNN, LSTM, GRU, bidirectional, BERT, and the Convolutional 1D model with the Maxout layer. The results of a comparison of the classifiers that are currently available and how they work are shown in an effective way.