  Weakly-supervised text classification has received much attention in recent
years for it can alleviate the heavy burden of annotating massive data. Among
them, keyword-driven methods are the mainstream where user-provided keywords
are exploited to generate pseudo-labels for unlabeled texts. However, existing
methods treat keywords independently, thus ignore the correlation among them,
which should be useful if properly exploited. In this paper, we propose a novel
framework called ClassKG to explore keyword-keyword correlation on keyword
graph by GNN. Our framework is an iterative process. In each iteration, we
first construct a keyword graph, so the task of assigning pseudo labels is
transformed to annotating keyword subgraphs. To improve the annotation quality,
we introduce a self-supervised task to pretrain a subgraph annotator, and then
finetune it. With the pseudo labels generated by the subgraph annotator, we
then train a text classifier to classify the unlabeled texts. Finally, we
re-extract keywords from the classified texts. Extensive experiments on both
long-text and short-text datasets show that our method substantially
outperforms the existing ones
