Quantitative Discursive Analysis

(C) 2019 Mark M. Bailey, PhD

About

Quantitative Discursive Analysis (QDA) will convert bodies of text into mathematical graph objects built from noun phrases, where each noun or modifier becomes a vertex, and each edge is determined by how the nouns and vertexes are linked within phrases. The more central the noun is to the overall text content, the higher the centrality measure of that particular noun. Thus, the graph is a weighted representation of discursive content, making it more robust that simple keyword frequencies. This object can be used to mathematically compare the discursive content of two or more bodies of text. This is done by calculating the "resonance" between two bodies of text, where resonance is the cosine angle between the betweenness centralities of the intersection of all vertices. This resonance value is normalized between [0,1], where 0 indicates no discursive similarity, and 1 indicates perfect discursive similarity.

Updates

*2022-10-13: Updated resonance function to be more Pythonic.

More information

This tool is built on NetworkX and TextBlob. Please see relevant documentation for additional information on what other calculations can be done on NetworkX graph objects generated using this library.

Download

https://pypi.org/project/QDA/

Sample Usage

import QDA

Instantiate discursive object.

text_graph = QDA.discursive_object('This is a string of your text. For best results, this string should be at least as long as a typical news article.')

Calculate resonance between two discursive objects.

a = QDA.resonate(text_graph_1, text_graph_2) (noun phrase tuples : list)

Calculate resonance of discursive objects in series.

resonance_series = QDA.resonate_as_series(G_list) (resonance values : dict)

Calculate resonance between all members of a list.

d_community = QDA.discursive_community(G_list)
d_community.A (Resonance adjacency matrix : ndarray)
d_community.G (Graph objhect built from adjacency matrix : NetworkX object)