Text classification usually has the basic problem of presenting data with very high
dimension, so that the model formed is usually hampered because (1) training time …

Text classification typically suffers from the fundamental issue of latter-day data with a very
high dimension, which hinders the model developed because training time grows …

Neural network-based applications are recently shown promising results for text
classification. However, it is still challenging for the model to contemplate local features and …

Text classification also known as text categorization is a classical task in natural
languageprocessing. The main aim of text classification is assigning one or more predefined …

In this paper, the efficiency of two states of the art text classification techniques, ie,
Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for supporting …

Text classification is one of the major research areas in the field of Natural Language
Processing. This is the task of determining the category of the text based on its content. This …

Recurrent Neural Network (RNN) is a deep learning architecture commonly used to process
time series and sequence data. Various architectures have been developed to improve the …

Text classification is a classic task in the field of natural language processing. however, the
existing methods of text classification tasks still need to be improved because of the complex …

Text classification is one of the most critical areas of research in the field of natural language
processing (NLP). Recently, most of the NLP tasks achieve remarkable performance by …

Text classification which is a part of NLP is a grouping of objects in the form of text based on
certain characteristics that show similarities between one document and another. One of …