Alex Wermer-Colan

Resources

• Text Mining YouTube Comment Data with Wordfish in R

  EN

  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.

• Making an Interactive Web Application with R and Shiny

  EN

  This lesson demonstrates how to build a basic interactive web application using Shiny, a library (a set of additional functions) for the programming language R. In the lesson, you will design and implement a simple application, consisting of a slider which allows a user to select a date range, which will then trigger some code in R, and display a set of corresponding points on an interactive map.

• Introduction to Map Warper

  EN

  This lesson from Programming Historian introduces basic use of Map Warper for historical maps. It guides you from upload to export, demonstrating methods for georeferencing and producing visualizations.

• Displaying a Georeferenced Map in KnightLab's StoryMap JS

  EN

  In this lesson from Programming Historian, you will learn how to display a georeferenced map from Map Warper in KnightLab's StoryMap JS, an interactive web-based map and storytelling platform.

• Computer Vision for the Humanities: An Introduction to Deep Learning for Image Classification (Part 2)

  EN

  This is the second of a two-part lesson introducing deep learning based computer vision methods for humanities research. This lesson digs deeper into the details of training a deep learning based computer vision model. It covers some challenges one may face due to the training data used and the importance of choosing an appropriate metric for your model. It presents some methods for evaluating the performance of a model.

• Computer Vision for the Humanities: An Introduction to Deep Learning for Image Classification (Part 1)

  EN

  This is the first of a two-part lesson introducing deep learning based computer vision methods for humanities research. Using a dataset of historical newspaper advertisements and the fastai Python library, the lesson walks through the pipeline of training a computer vision model to perform image classification.

• Clustering and Visualising Documents Using Word Embeddings

  EN

  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.