Machine-learning-based text classification is one of the leading research areas and has a
wide range of applications, which include spam detection, hate speech identification …

The aim of this study is to provide an overview the state-of-the-art elements of text
classification. For this purpose, we first select and investigate the primary and recent studies …

With the advent of Web 2.0, people became more eager to express and share their opinions
on web regarding day-to-day activities and global issues as well. Evolution of social media …

This paper proposes hybrid feature selection approaches based on the Genetic Algorithm
(GA). This approach uses a hybrid search technique that combines the advantages of filter …

Text classification is an important and classical problem in natural language processing.
Recently, Graph Neural Networks (GNNs) have been widely applied in text classification …

High dimensionality of the feature space is one of the major concerns owing to
computational complexity and accuracy consideration in the text clustering. Therefore …

Text classification results can be hindered when just the bag-of-words model is used for
representing features, because it ignores word order and senses, which can vary with the …

Due to the ever increasing number of documents in the digital form, automated text
clustering has become a promising method for the text analysis in last few decades. A major …

Aiming at fostering the transition towards a sustainable climate-neutral economy, the
German Federal Government (GFG) intends to promote the transition towards a sustainable …

Most of the previous studies related on different term weighting emphasize on the document-
indexing-based and four fundamental information elements-based approaches to address …