  Graph Convolutional Networks (GCN) have been effective at tasks that have
rich relational structure and can preserve global structure information of a
dataset in graph embeddings. Recently, many researchers focused on examining
whether GCNs could handle different Natural Language Processing tasks,
especially text classification. While applying GCNs to text classification is
well-studied, its graph construction techniques, such as node/edge selection
and their feature representation, and the optimal GCN learning mechanism in
text classification is rather neglected. In this paper, we conduct a
comprehensive analysis of the role of node and edge embeddings in a graph and
its GCN learning techniques in text classification. Our analysis is the first
of its kind and provides useful insights into the importance of each graph
node/edge construction mechanism when applied at the GCN training/testing in
different text classification benchmarks, as well as under its semi-supervised
environment.
