The rapid increment in internet usage has also resulted in bulk gerenation of text data.
Therefore, investigation of new techniques for automatic classification of textual content is …

Traditional manual text classification method has been unable to cope with the current huge
amount of data volume. The improvement of deep learning technology also accelerates the …

Feature extraction in the field of Text Processing or Natural Language Processing (NLP) has
its own challenges due to the characteristics of unstructured text. Thus, the selection of the …

The use of word embedding models and deep learning algorithms are currently the most
common and popular trends to enhance the overall performance of a text classification …

Text classification is one of the research hotspots in the field of Natural Language
Processing (NLP). In this paper, TextCNN model based on Convolutional Neural Network …

Nowadays, the volume of data offered on the Internet is growing every moment, and the
necessity to analyze these data and convert to useful information increased. There are …

Text classification has a growing interest among NLP researchers due to its tremendous
availability on online platforms and emergence on various Web 2.0 applications. Recently …

Categorizing Arabic text documents is considered an important research topic in the field of
Natural Language Processing (NLP) and Machine Learning (ML). The number of Arabic …

This paper presents, TextConvoNet, a novel Convolutional Neural Network (CNN) based
architecture for binary and multi-class text classification problems. Most of the existing CNN …

Text classification is the task of forming semantic groups of text documents by assigning
predefined class labels. It has wide range of real-life applications in various domains such …