Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …

Automatic text classification is the task of organizing documents into pre-determined classes,
generally using machine learning algorithms. Generally speaking, it is one of the most …

Methods for document clustering and topic modelling in online social networks (OSNs) offer
a means of categorising, annotating and making sense of large volumes of user generated …

The growth of data volume has changed cybersecurity activities, demanding a higher level
of automation. In this new cybersecurity landscape, text mining emerged as an alternative to …

Software testing is still a manual process in many industries, despite the recent
improvements in automated testing techniques. As a result, test cases (which consist of one …

There has been a meteoric rise in the total amount of digital texts as a direct result of the
proliferation of internet access. As a direct result of this, document clustering has evolved …

In this paper, we show how selecting and combining encodings of natural and mathematical
language affect classification and clustering of documents with mathematical content. We …

Document clustering is a well-known text-mining method that assists in the categorization
and comprehension of textual data. Document clustering is vital in areas like information …

Abstract The China-Pakistan Economic Corridor (CPEC) is a flagship project of the Belt and
Road Initiative. How Pakistani media coverage shapes the CPEC is related to public …

Document clustering allows a user to group semantically similar documents. It has been an
interesting research area for the past many years and various methods and techniques have …