> For the complete documentation index, see [llms.txt](https://entreprecariat.gitbook.io/entreprecariat/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://entreprecariat.gitbook.io/entreprecariat/network-analysis.md).

# 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](https://matplotlib.org/). 

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

{% file src="/files/V66ggjQlSAHdvE5dbOKL" %}