  Text document classification is an important task for diverse natural
language processing based applications. Traditional machine learning approaches
mainly focused on reducing dimensionality of textual data to perform
classification. This although improved the overall classification accuracy, the
classifiers still faced sparsity problem due to lack of better data
representation techniques. Deep learning based text document classification, on
the other hand, benefitted greatly from the invention of word embeddings that
have solved the sparsity problem and researchers focus mainly remained on the
development of deep architectures. Deeper architectures, however, learn some
redundant features that limit the performance of deep learning based solutions.
In this paper, we propose a two stage text document classification methodology
which combines traditional feature engineering with automatic feature
engineering (using deep learning). The proposed methodology comprises a filter
based feature selection (FSE) algorithm followed by a deep convolutional neural
network. This methodology is evaluated on the two most commonly used public
datasets, i.e., 20 Newsgroups data and BBC news data. Evaluation results reveal
that the proposed methodology outperforms the state-of-the-art of both the
(traditional) machine learning and deep learning based text document
classification methodologies with a significant margin of 7.7% on 20 Newsgroups
and 6.6% on BBC news datasets.
