  In this paper, we explore SPPIM-based text classification method, and the
experiment reveals that the SPPIM method is equal to or even superior than SGNS
method in text classification task on three international and standard text
datasets, namely 20newsgroups, Reuters52 and WebKB. Comparing to SGNS, although
SPPMI provides a better solution, it is not necessarily better than SGNS in
text classification tasks. Based on our analysis, SGNS takes into the
consideration of weight calculation during decomposition process, so it has
better performance than SPPIM in some standard datasets. Inspired by this, we
propose a WL-SPPIM semantic model based on SPPIM model, and experiment shows
that WL-SPPIM approach has better classification and higher scalability in the
text classification task compared with LDA, SGNS and SPPIM approaches.
