Computing the semantic similarity between documents by the copula-based econometric models

Abstract

Semantic similarity is important information with which decision-makers can cluster, classify, or compare documents in text mining. Statistical and topological methods are two major ways to determine semantic similarity. However, conventional methods ignore the time factor when calculating the similarity between documents. It should be highlighted that narrative emotions play a critical role in comparing documents. In this paper, copula-based econometric models, including ARMA and GARCH families, are used to calculate the narrative semantic similarity between documents.