Granular computing involves a comprehensive process that encompasses theories,
methodologies, and techniques to solve complex problems, rather than being just an …

Text categorization is an interesting application of machine learning covering a wide range
of possible applications, from document management systems to web mining. In designing …

Deep learning--based models have surpassed classical machine learning--based
approaches in various text classification tasks, including sentiment analysis, news …

Over the last decade Deep learning-based models surpasses classical machine learning
models in a variety of text classification tasks. The primary challenge with text classification …

The text classification task is an important application in natural language processing. At
present, deep learning models, such as convolutional neural network and recurrent neural …

The task of text classification using Bidirectional based LSTM architectures is
computationally expensive and time consuming to train. For this, transformers were …

Despite the fact that text classification has grown in relevance over the last decade, there are
a plethora of approaches that have been created to meet the difficulties related with text …

Recently, there are unprecedented data growth originating from different online platforms
which contribute to big data in terms of volume, velocity, variety and veracity (4Vs). Given …

Abstract Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN) based
text classification algorithms currently in use can successfully extract local textual features …

Recently, deep learning has achieved impressive success in text mining and Natural
Language Processing tasks. Bert is one of the remarkably rewarding deep learning models …