Majority of the existing text classification algorithms are based on the “bag of words”(BOW)
approach, in which the documents are represented as weighted occurrence frequencies of …

Text classification is a widely studied topic in the area of machine learning. A number of
techniques have been developed to represent and classify text documents. Most of the …

Current classification methods are based on the ldquobag of wordsrdquo (BOW)
representation, which only accounts for term frequency in the documents, and ignores …

Which features are the most important for the text classification tasks? In the automatic text
categorization area, several studies seek answers to this question. In this paper, a feature …

Text classification has been widely used to assist users with the discovery of useful
information from the Internet. However, traditional classification methods are based on the …

Successful text classification is highly dependent on the representations used. Currently,
most approaches to text classification adopt thebag-of-words' document representation …

Text categorization, especially short text categorization, is a difficult and challenging task
since the text data is sparse and multidimensional. In traditional text classification methods …

In news portals, text category information is needed for news presentation. However, for
many news stories the category information is unavailable, incorrectly assigned or too …

Content based Document Classification is one of the biggest challenges in the context of
free text mining. Current algorithms on document classifications mostly rely on cluster …

The exponential growth of text documents available on the Internet has created an urgent
need for accurate, fast, and general purpose text classification algorithms. However, the" …