A Novel Method of Network Text Analysis

Starling Hunter

doi:10.4236/ojml.2014.42028

Abstract

This paper describes a novel method of network text analysis, one that involves a new approach to 1) the selection of words from a text, 2) the aggregation of those words into higher-order concepts, 3) the kind of the relationship that establishes statements from pairs of concepts and 4) the extraction of meaning from the text network formed by these statements. After describing the method, I apply it to a sample of the seven most recent winners of the Academy Award for Best Original Screenplay―Little Miss Sunshine, Juno, Milk, The Hurt Locker, The King’s Speech, Midnight in Paris, and Django Unchained. Consistent with prior research, I demonstrate that structure encodes meaning. Specifically, it is shown that statements associated with a text network’s least constrained nodes are consistent with themes in the films’ synopses found on Wikipedia, the International Movie Database, and Rotten Tomatoes.

Highlights

Diesner & Carley (2005: p. 83) employ the term network text analysis (NTA) to describe a wide variety of “computer supported solutions” that enable analysts to “extract networks of concepts” from texts and to discern the “meaning” represented or encoded therein
A second important assumption is that the position of concepts within a text network provides insight into the meaning or prominent themes of the text as a whole
Approaches to NTA differ with regard to how these steps are performed, as well as other dimensions like the level of automation or computer support, the linguistic unit of analysis, and the degree and basis of concept generalization

Summary

Introduction

Diesner & Carley (2005: p. 83) employ the term network text analysis (NTA) to describe a wide variety of “computer supported solutions” that enable analysts to “extract networks of concepts” from texts and to discern the “meaning” represented or encoded therein. The key underlying assumption of such methods or solutions, they assert, is that the “language and knowledge” embodied in a text may be “modeled” as a network “of words and the relations between them” (ibid, emphasis added). A second important assumption is that the position of concepts within a text network provides insight into the meaning or prominent themes of the text as a whole. Considered, creating networks from texts has two basic steps: 1) the assignment of words and phrases to conceptual categories and 2) the assignment of links to pairs of concepts. Approaches to NTA differ with regard to how these steps are performed, as well as other dimensions like the level of automation or computer support, the linguistic unit of analysis (e.g. noun or verbs), and the degree and basis of concept generalization.

Methods

Findings

Discussion

Conclusion