(C) 2019 Mark M. Bailey, PhD
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.
*2022-10-13: Updated resonance function to be more Pythonic.
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.
import QDA
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.')
a = QDA.resonate(text_graph_1, text_graph_2) (noun phrase tuples : list)
resonance_series = QDA.resonate_as_series(G_list) (resonance values : dict)
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)