  Text categorization (TC) is the task of automatically organizing a set of
documents into a set of pre-defined categories. Over the last few years,
increased attention has been paid to the use of documents in digital form and
this makes text categorization becomes a challenging issue. The most
significant problem of text categorization is its huge number of features. Most
of these features are redundant, noisy and irrelevant that cause over fitting
with most of the classifiers. Hence, feature extraction is an important step to
improve the overall accuracy and the performance of the text classifiers. In
this paper, we will provide an overview of using principle component analysis
(PCA) as a feature extraction with various classifiers. It was observed that
the performance rate of the classifiers after using PCA to reduce the dimension
of data improved. Experiments are conducted on three UCI data sets, Classic03,
CNAE-9 and DBWorld e-mails. We compare the classification performance results
of using PCA with popular and well-known text classifiers. Results show that
using PCA encouragingly enhances classification performance on most of the
classifiers.
