A graph-based approach to document classification is described in this paper. The graph
representation offers the advantage that it allows for a much more expressive document …

In this paper, we consider the task of text categorization as a graph classification problem.
By representing textual documents as graph-of-words instead of historical n-gram bag-of …

Text classification constitutes a popular task in Web research with various applications that
range from spam filtering to sentiment analysis. To address it, patterns of co-occurring words …

Automatic classification of data items, based on training samples, can be boosted by
considering the neighborhood of data items in a graph structure (eg, neighboring documents …

For information retrieval and text-mining, a robust scalable framework is required to
represent the information extracted from documents and enable visualization and query of …

Text Mining is the discovery of valuable, yet hidden, information from the text document. Text
classification (Also called Text Categorization) is one of the important research issues in the …

Automatic text categorization has always been an important application and research topic
since the inception of digital documents. Today, text categorization is a necessity due to the …

Most existing text classification methods (and text mining methods at large) are based on
representing the documents using the traditional vector space model. We argue that …

Text classification (aka text categorisation) is an effective and efficient technology for
information organisation and management. With the explosion of information resources on …

Text classification is the process of classifying documents into predefined categories based
on their content. It is the automated assignment of natural language texts to predefined …