Text categorization using weighted hyper rectangular keyword extraction

Abstract

Text categorization is an important research field that finds many applications nowadays. It is usually performed in two steps: feature extraction and classification. In the feature extraction step, discriminating keywords are extracted in order to distinguish between different categories of documents. In the classification step, the extracted keywords are fed to a classifier in order to detect the category of each document. In this paper, we use the hyper rectangle method which represents the corpus of documents using a binary relation in which the documents correspond to objects and words to attributes. The hyper rectangle method extracts a tree of keywords such that most discriminative keywords are at the top levels and less discriminative keywords are in the deep levels. We are particularly interested to study different proposed weighting metrics that yield different orderings of keywords. We study how these weighting metrics impact the categorization performance. For the classification step we used both a logistic regression and random forests classifiers. We tested our method on both the 20 newsgroups dataset as well as the Reuters R8 dataset. Our method achieves high performance on both datasets which compete very well with state-of-the-art methods.