1. Represents text documents as graph-of-words and extracts subgraph features through frequent subgraph mining to classify texts as a graph classification problem. 2. Uses gSpan algorithm to efficiently mine frequent subgraphs from the graph-of-words and selects the optimal minimum support threshold using the elbow method. 3. Evaluates the approach on four datasets, achieving improved accuracy over bag-of-words models by extracting long-distance n-gram features through subgraph mining.