Semantic Network Analysis Pipeline-Interactive Text Mining Framework for Exploration of Semantic Flows in Large Corpus of Text

Historical topic modeling and semantic concepts exploration in a large corpus of unstructured text remains a hard, opened problem. Despite advancements in natural languages processing tools, statistical linguistics models, graph theory and visualization, there is no framework that combines these piece-wise tools under one roof. We designed and constructed a Semantic Network Analysis Pipeline (SNAP) that is available as an open-source web-service that implements work-flow needed by a data scientist to explore historical semantic concepts in a text corpus. We define a graph theoretic notion of a semantic concept as a flow of closely related tokens through the corpus of text. The modular work-flow pipeline processes text using natural language processing tools, statistical content narrowing, creates semantic networks from lexical token chaining, performs social network analysis of token networks and creates a 3D visualization of the semantic concept flows through corpus for interactive concept exploration. Finally, we illustrate the framework's utility to extract the information from a text corpus of Herman Melville's novel Moby Dick, the transcript of the 2015-2016 United States (U.S.) Senate Hearings on Environment and Public Works, and the Australian Broadcast Corporation's short news articles on rural and science topics.