Programming Historian offers novice-friendly, peer-reviewed lessons that help humanists learn a wide range of digital tools, techniques, and workflows to facilitate research and teaching.
This lesson covers tokenization, part-of-speech tagging, and lemmatization, as well as automatic language detection, for non-English and multilingual text. You’ll learn how to use the Python packages NLTK, spaCy, and Stanza to analyze a multilingual Russian and French text.
In this lesson, you will learn how to download YouTube video comments and use the R programming language to analyze the dataset with Wordfish, an algorithm designed to identify opposing ideological perspectives within a corpus.
In this lesson, you’ll learn computer vision and machine learning principles for object recognition, and how to apply these principles using Python to recognize and classify smiling faces in historical photographs.
A digital gazetteer records information associated with specific places. This lesson teaches you how to create a gazetteer from a historical text, using the Linked Places Delimited (LP-TSV) format.
Word embeddings allow you to analyze the usage of different terms in a corpus of texts by capturing information about their contextual usage. Through a primarily theoretical lens, this lesson will teach you how to prepare a corpus and train a word embedding model. You will explore how word vectors work, how to interpret them, and how to answer humanities research questions using them.
This lesson demonstrates how to create interactive data visualizations in Python with Plotly’s open-source graphing libraries using materials from the Historical Violence Database.
Tools for machine transcription of handwriting are practical and labour-saving if you need to analyse or present text in digital form. This lesson will explain how to write a Python program to transcribe handwritten documents using Microsoft’s Azure Cognitive Services, a commercially available service that has a cost-free option for low volumes of use.
This lesson uses word embeddings and clustering algorithms in Python to identify groups of similar documents in a corpus of approximately 9,000 academic abstracts. It will teach you the basics of dimensionality reduction for extracting structure from a large corpus and how to evaluate your results.
This lesson demonstrates how to use the Python library spaCy for analysis of large collections of texts. This lesson details the process of using spaCy to enrich a corpus via lemmatization, part-of-speech tagging, dependency parsing, and named entity recognition. Readers will learn how the linguistic annotations produced by spaCy can be analyzed to help researchers explore meaningful trends in language patterns across a set of texts.
This lesson teaches you how to obtain and analyse narrative texts for patterns of sentiment and emotion. The 'syuzhet' sentiment analysis algorithm, along with the programming language R, will be used, demonstrating the techniques to allow learners to follow along.
This lesson provides a beginner-friendly introduction to convolutional neural networks (CNNs) for image classification. The tutorial provides a conceptual understanding of how neural networks work by using Google’s Teachable Machine to train a model on paintings from the ArtUK database. This lesson also demonstrates how to use Javascript to embed the model in a live website.
In this lesson, you will use Qt Designer and Python to design and implement a simple graphical user interface and application to merge PDF files. This lesson also demonstrates how to package the application for distribution to other personal computers.
