Graph-Based Term Weighting for Text Categorization

Text categorization is an important task with plenty of applications, ranging from sentiment analysis to automated news classification. In this paper, we introduce a novel graph-based approach for text categorization. Contrary to the traditional Bag-of-Words model for document representation, we consider a model in which each document is represented by a graph that encodes relationships between the different terms. The importance of a term to a document is indicated using graph-theoretic node centrality criteria. The proposed weighting scheme is able to meaningfully capture the relationships between the terms that co-occur in a document, creating feature vectors that can improve the categorization task. We perform experiments in well-known document collections, applying popular classification algorithms. Our preliminary results indicate that the proposed graph-based weighting mechanism is able to outperform existing frequency-based term weighting criteria, under appropriate parameter setting.