InfraNodus: Generating Insight Using Text Network Analysis

Abstract

In this paper we present a web-based open source tool and a method for generating insight from any text or discourse using text network analysis. The tool (InfraNodus) can be used by researchers and writers to organize and to better understand their notes, to measure the level of bias in discourse, and to identify the parts of the discourse where there is a potential for insight and new ideas. The method is based on text network analysis algorithm, which represents any text as a network and identifies the most influential words in a discourse based on the terms' co-occurrence. Graph community detection algorithm is then applied in order to identify the different topical clusters, which represent the main topics in the text as well as the relations between them. The community structure is used in conjunction with other measures to identify the level of bias or cognitive diversity of the discourse. Finally, the structural gaps in the graph can indicate the parts of the discourse where the connections are lacking, therefore highlighting the areas where there's a potential for new ideas. The tool can be used as stand-alone software by end users as well as implemented via an API into other tools. Another interesting application is in the field of recommendation systems: structural gaps could indicate potentially interesting non-trivial connections to any connected datasets.