Fast training of a graph boosting for large-scale text classification

Abstract

This paper proposes a fast training method for graph classification based on a boosting algorithm and its application to sentimental analysis with input texts represented by graphs. Graph format is very suitable for representing texts structured with Natural Language Processing techniques such as morphological analysis, Named Entity Recognition, and parsing. A number of classification methods which represent texts as graphs have been proposed so far. However, many of them limit candidate features in advance because of quite large size of feature space. Instead of limiting search space in advance, we propose two approximation methods for learning of graph-based rules in a boosting. Experimental results on a sentimental analysis dataset show that our method contributes to improved training speed. In addition, the graph representation-based classification method exploits rich structural information of texts, which is impossible to be detected when using other simpler input formats, and shows higher accuracy.