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Text Analysis with Python

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This course is divided into four Topics, each of which will focus on a different aspect of Python:
*Topic 1 will demonstrate how to install the necessary software on your computer or laptop to start engaging with Python.
*Topic 2 will tackle the basics of Python, teaching you how to write and fix simple pieces of code.
*Topic 3 will focus on the more complex task of text analysis using Python and will provide you with pieces of code that you can use for yourself.
*Topic 4 will explore the world of Notebooks, outlining how to make yours public, if you choose.

By the end of this Intensive, you should:
* Be able to write and fix simple pieces of code.
* Have assembled a ‘toolkit’ as such, from the bits of code we have written for you along the way, which you can use to perform computational analysis on all kinds of texts; from Shakespeare, to historical newspapers, to court transcripts – as long as Python can read it, the possibilities are endless!
Key InformationPoints50Effort12 hoursPrerequisitesnoneRelease Date7th December 2020
Author Info

Catherine Elkin is a third-year PhD student of English and History. Her thesis topic focuses on nineteenth-century representations of baby farming. Learning aspects of digital humanities such as coding has enabled her to perform computational analysis on large numbers of historical documents.

Maartje Weenink is a PhD student at Manchester Metropolitan University and specializes in computational analysis of Gothic texts. She uses word-embeddings and a large corpus of early Gothic fiction to analyse the development of trends in the Gothic mode. Her use of the methodology of the Digital Humanities is aimed at synthesizing qualitative and quantitative approaches to genre.

Dave Mee is a practice-based PhD student at MMU’s Department of Media, researching how items of time-based content are used to radicalize members of online communities with ‘fake news’. Python is the glue that binds quantitative data capture, analysis and mapping together, and generate visualizations around longitudinal patterns. After some 40 years of programming, he can firmly say he hates it, but hasn’t found an alternative.