Network Analysis

Actors

Entities are words in a text that correspond to a specific type of data. They can be numerical, such as cardinal numbers; temporal, such as dates; nominal, such as names of people and places; and political, such as geopolitical entities (GPE). In short, an entity can be anything the designer wishes to designate as an item in a text that has a corresponding label. (Mattingly, 2021)

We wanted to inspect the people (nominal entities), organizations and places (GPE) involved by Silvio Lorusso in his book, i.e., all the entities related to the world of entreprecariat-ism, by following this workflow:

• Tokenization

• POS tagging

• NER recognition

First we extracted all the Named-Entities, by means of the Python library Spacy. We did this for two groups of entities, separately:

• People

• Organization

Then, we proceded building a network from the people/organizations who co-occured in the sentences. Lastly, we inspected the most cited people/organizations.

Both the graph visualization and the horizontal bar chart have been produced through the python library matplotlib.

The code used to extract the entities and build both the the networks and the bar charts is available below.

2KB

NERAndNetworks.py