Short-Text Feature Expansion and Classification Based on Non-negative Matrix Factorization

In this paper, a Non-negative Matrix Factorization Feature Expansion (NMFFE) approach was proposed to overcome the feature-sparsity issue when expanding features of short-text. Firstly, we took the internal relationships of short texts and words into account when segmenting words from texts and constructing their relationship matrix. Secondly, we utilized Dual regularization Non-negative Matrix Tri-Factorization algorithm (DNMTF) to obtain the words clustering indicator matrix, which was used to get the feature space by dimensionality reduction methods. Thirdly, words with close relationship were selected out from the feature space and added into the short-text in order to solve the sparsity issue. The experimental results showed that the accuracy of short text classification of our NMFFE algorithm increased 25.77%, 10.89% and 1.79% on three datasets: Web snippets, Twitter sports and AGnews respectively compared with Word2Vec algorithm and Char-CNN algorithm. It indicated that the NMFFE algorithm was better than BOW algorithm and the Char-CNN algorithm in terms of classification accuracy and algorithm robustness.