This lesson provides an introduction to variables, operators, loops, lists, and dictionaries. Students will learn how to use lists and dictionaries to manipulate data in their programs. By the end of the lesson, students will have a foundational understanding of Python’s core concepts.

In this lesson, students will learn how to use the Natural Language Toolkit (nltk) to download a corpus and then use Python and regular expressions to clean the text data. Students will learn how to remove unwanted characters and symbols, tokenise the text into individual words, and how to remove stop words. This lesson will equip students with the skills to clean and preprocess text data for further analysis and natural language processing tasks.

This is a continuation of lesson 2 where you will find how to use more advanced Python data structures and we will introduce how to use the Guttenberg project Python library to download corpora and how to clean text. Finally, students will learn what is and how to use the panda’s library for working and processing very large corpora.

In this lesson, students will use the natural language processing library, spaCy, to extract all the “scary” verbs from a corpus of horror books. They will then use the similarity function in spaCy to determine which verbs are most closely related to the concept of fear. Additionally, they will determine which book is the “scariest” by calculating the ratio of scary verbs to total words in each book. This exercise will provide students with an understanding of how natural language processing can be used to analyse and compare different works of literature based on a specific set of criteria.

Embedding spaces and computational literary studies have emerged as a fruitful convergence of computer science and literary analysis. This presentation introduces the concept of embedding spaces, which represent textual data as high-dimensional vectors, capturing semantic relationships and contextual information. Specifically, word embeddings enable tasks such as sentiment analysis and authorship attribution, while sentence and document embeddings facilitate analysis at larger text units. By leveraging computational methods, researchers can delve into the complexities of literature, offering quantitative insights and novel research avenues. This interdisciplinary approach holds great promise for revolutionising the study of literature and deepening our understanding of its intricacies.

This is an introductory overview of the field of stylometry and multivariate text analysis. We discuss classic approaches to text representation, such as bag of words, and show the ability of word frequencies to reflect meaningful cultural and social conditions of texts: genre, chronology and authorship.

R and RStudio

The most important thing to do would be installing R and RStudio on your machine. We will use the stylo library for most of the day; if you want, you can look at step-by-step introduction to stylo for beginners, or at the more extensive HOW TO, but we will cover all the basics. NB. In case you don’t / can’t install R and stylo locally, there will be an option to run analysis from Colab, just be aware it will require more coding, not less coding, because of the stylo’s interface that doesn’t work in Colab. You are free to bring your own collection of texts to the workshop, but you also can find plain text fiction collections in various languages on the Computational Stylistics website.

Materials

We will use GDrive folder that holds all necessary materials. Your options are:

• Download it and work locally
• Download -> re-upload folder to work in Colabs’ virtual machine with R (or just open the .ipynb notebook, then File -> Save a copy in Drive. It will create a copy of the notebook on your own Drive.)

To copy folder to your own GDrive, do this:

• Download CLS_Madrid_Folder
• Extract the downloaded .zip
• Upload the extracted folder back to your GDrive

This session introduces the ‘stylo’ library for R that allows to perform different stylometric analysis on a collection of documents. We quickly introduce R language and go over a graphical user interface of ‘stylo’ to show practicalities of feature selection, distance metrics, cluster analysis and sampling.

Note - Download instructions for software and materials can be found in Session 5

In this session, we look at the ideas of the word ‘keyness’ and the ways to understand which features differ between texts and corpora. We also show how to trace features that might underlie text groupings and clusters and detect a potential bias, or systematic error in a corpus.

Note - Download instructions for software and materials can be found in Session 5

In this session, we will try to find out whether the Old Spanish version of the Book of Alexander was written by an author known as Gonzalo de Berceo, as it is stated in one manuscript, or not. We will gather the data from the Old Spanish Textual Archive (OSTA), a corpus of Old Spanish Texts lemmatised and PoS tagged. Then we will use the stylo package to find it out. Afterwards, we will try to see if the rhyming words are a good element to establish the authorship of the Book of Alexander.

This session aims to introduce visualisation grammars and their applications to accelerate exploratory data analysis in digital humanities projects by allowing users to create interactive visualisations with minimal effort in a Jupyter notebook. To this end, a real application will be built by replicating the design process of a visualisation system based on machine-annotated textual data, in a similar setup to many digital humanities research projects.

You can download the course materials for this session here: CLICK HERE

Corpus data should be kept FAIR: findable, accessible, interoperable and reusable. This lecture will show how to do that using an example set-up in which the LINDAT repository is used for findability (as well as long-term preservation), TEITOK for accessibility (as well as maintenance and annotation), and TEI for interoperability and reusability. And we will show how to interact with these tools and standard both via a GUI (online) and using an API (to interact programmatically).