Summary and Setup

Welcome

This lesson is about the fundamentals of Natural Language Processing (NLP) in Python, with applications in the Humanities and Social Sciences.

It will equip you with the foundational skills and knowledge needed to carry over text-based research projects. The lesson is designed specifically with researchers in the Humanities and Social Sciences in mind, but is also applicable to other fields of research.

Prerequisites

Before joining this course, participants should have:

  • Basic Python programming skills
  • Basic knowledge of Git and GitHub

Software Setup

Installing Python

Python is a popular language for scientific computing, and a frequent choice for machine learning as well. To install Python, follow the Beginner’s Guide or head straight to the download page.

Please set up your python environment at least a day in advance of the workshop. If you encounter problems with the installation procedure, ask your workshop organizers via e-mail for assistance so you are ready to go as soon as the workshop begins.

Installing the required packages

Pip is the package management system built into Python. Pip should be available in your system once you installed Python successfully.

Jupyter Lab

We will teach using Python in Jupyter Lab, a programming environment that runs in a web browser. Jupyter Lab is compatible with Firefox, Chrome, Safari and Chromium-based browsers. Note that Internet Explorer and Edge are not supported. See the Jupyter Lab documentation for an up-to-date list of supported browsers.

To start Jupyter Lab, open a terminal (Mac/Linux) or Command Prompt (Windows) and type the command:

jupyter lab

Data Sets

For the episode 01: preprocessing and word embeddings (Word2Vec):

  • Download the Algemeen Dagblad from July 21 1969 as txt file from Delpher. To do so, click on the link and navigate to the right hand side of the web page. There you’ll find an icon with an arrow pointing down:
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Click on this icon and select txt among the downloading options

  • Download Word2Vec models trained on 6 national Dutch newspaper data spanning a time period from 1950 to 1989 (Wevers, M., 2019). These models are available on Zenodo.