Abstract:

Based on the theoretical principles of latent semantic analysis, and combined with support vector machine (SVM) classifier performance, we propose a local latent semantic analysis algorithm (SLLSA) to solve multiple problems about classification effect and performance optimization in web text categorization and representation. We introduce category information into singular value decomposition (SVD), analyze the local features of feature words, uses the SVM classifier to compute the dependency degree, and select local areas . Experimental results show that the SLLSA algorithm effectively solves the key problems of SVD, greatly improves the effectiveness of web text classification, and better represents  the latent semantic space of web texts.

Key words: text classification;local latent semantic analysis;SVM;SVD;S-LLSA