books and such

• Debates in the Digital Humanties, a book series. All of the books are available online an have open annotations.


• The data-sitters club. A fun and colloquial guide to digital humanities computational text analysis


• the Data Notebook, an online suite of open interactive resources that provides instructional materials for introductory data analytics and data visualization approaches relevant to a wide range of subjects and disciplines. Specifically, this book focuses on principles related to data storytelling, and provides tangible research steps and include case studies, mini-lessons, and interactive instructional components. By Kenton Rambsy and Peace Ossom Ossom-Williamson.


• Speech and Language Processing, 3rd edition draft, by Dan Jurafsky and James H. Martin


• Introduction to Earth Data Science Textbook by the Earth Lab at the University of Colorado Boulder. A good, introduction to python.


• Use Data for Earth and Environmentla Science in Open Source Python Textbook also from the Earth Lab at CU Boulder. Includes a chapter on APIs and working with twitter data in python.

software and data

• A collection of digital humanities data sets, curated by .txtlab a laboratory for cultural analytics at McGill University.


• GloVe Global Vectors for Word Representation


• spaCy, a python library for NLP. Fast, efficient.


• Tensorflow's projector tool


• The NRC Valence, Arousal, and Dominance (NRC-VAD) Lexicon by Saif M. Mohammad.


• Code for the US History Textbook project

courses

• Introduction to Computational Literary Analysis, Summer 2022 a course by Jonathan Reeve. Highly recommended.


• Introduction to Digital Humanites a course at the CUNY graduate center by Matthew K. Gold.


• CSC321 neural networks and Machine Learning, a course at the University of Toronto. Not specific to text, but has many text examples. Includes some lecture notes, slides, and python notebooks.


• Meaningful Text Analysis with Word Embeddings by Jonathan Reeve. Videos for the course can be found here

tutorials, etc

• Creating visuals with NLTK's FreqDist by Darius Fuller. Shows how to make some better-looking frequency distribution charts.


• The Illustrated Word2vec - A Gentle Intro to Word Embeddings in Machine Learning, a helpful, short video by Jay Alammar. He has lots of other explainer videos on his youtube channel. Jay's artcile, The Illustrated Word2vec, is also excellent and highly recommended.