Concept mining in online forums using self-corpus-based augmented text clustering

Abstract

This paper proposes a self-corpus-based text augmentation technique with clustering for concept mining in a discussion forum. Sparseness in text data, which challenges the distance and density measures in determining the concepts in a corpus, is handled through self-corpus-based document expansion via matrix factorization. Experiments with a real-world dataset show that the proposed method is able to infer useful concepts.