The text analysis resources here cover topics such as installing computer programming languages (like R and Python), running exploratory scripts of word tokenizations and counts, and more advanced approaches like topic modeling and word embedding models.

Getting Started

• Where to Start – A guide on how to start text mining. Written by Ted Underwood of the University of Illinois, Urbana-Champaign.
• Text Analysis Introduction – Basic guide to introductory text analysis from “Tooling Up for Digital Humanities.”
• Natural Language Processing with Deep Learning – Material from Stanford University’s “Natural Language Processing with Deep Learning” course.
• Text Mining with R: A Tidy Approach – Textbook by Julia Silge and David Robinson.
• Computational Text Analysis for Social Science – Article on social science text analysis methodology usage by Brendan O’Connor, David Bamman, and Noah A. Smith of Carnegie Mellon University.

Python

• Python Programming for the Humanities – Interactive tutorial and introduction into Python programming for the humanities by Folgert Karsdorp.
• Python for Informatics – Textbook by Charles Severance that is an applied but comprehensive introductory Python text with sections on text parsing.
• Download and Install Python – Website for downloading and installing Python.
• Download and Install PyCharm – Website for downloading an installing PyCharm, an Integrated Development Environment (IDE) for Python.
• Download and Install IPython – Website for downloading and installing IPython, an interactive shell for Python programming.
• The Hitchhiker’s Guide to Python – A textbook for both novice and expert developers by Kenneth Reitz and Tanya Schlusser.
• Python Text Analysis Tutorial – A list of tutorials on Python and text analysis assembled by Neal Caren of the University of North Carolina, Chapel Hill.
• Codeacademy for Python – Online teaching resources for learning Python and other programming languages.
• Google Python Tutorial – Google’s free online Python programming class.
• Machine Learning with Sci-Kit Learn – Video tutorials for machine learning in python with the Sci-Kit Learn Package.
• Python NLP Course – Yandex Data School’s Natural Language Processing in Python Course.
• Web-Scraping Tutorial– Tutorial on Web-Scraping with Python.
• Python Text Analysis Course – Material on GitHub for Laura K. Nelson’s text analysis course.
• Python for Everyone – Open source Python textbook with exercises.
• Practice Python – Beginner Python exercises that comes with discussion topics.

R

• Text Analysis With R for Students of Literature – Matthew Lee Jockers introductory text with PDF available through NEU Library.
• Download and Install R – Website for downloading and installing the R programming language.
• Download and Install RStudio – Website for downloading and installing RStudio, an Integrated Development Environment (IDE) for R.
• RSeek – A search tool for finding resources on R programming.
• Simple Data Types in R – Information on basic data types in the R programming language.
• Humanities Data Analysis Class – Material from Ben Schmidt’s graduate seminar on data analysis for the humanities.
• Managing and Manipulating Data in R – Material from a UCLA programming with R course.
• Text as Data R Course – Material on using textual data in R by Chris Bail of Duke University.
• A Light Introduction to Text Analysis in R – An introduction and overview of text analysis tools in R.
• Data Science Specialization Course – A broad course in R covering multiple data science techniques like statistical inference, machine learning, regression models, exploratory analysis, and others.
• Computational Statistics in R Course – Material from Northeastern professor Nick Beauchamp’s Computational Social Science course.

Topic Modeling

• Journal of Digital Humanities’s Special Issue  – Special issue of JDH specifically on Topic Modeling in the humanities published in 2012.
  • Topic Modeling: A Basic Introduction – Introductory article by Megan R. Brett from JDH’s special issue explaining the basic concepts of topic modeling.
  • Words Alone – Article on Latent Dirichlet Allocation’s (LDA’s) limitations by Ben Schmidt.
• Topic Modeling Made Just Simple Enough – An introduction to topic modeling written by Ted Underwood of University of Illinois, Urbana-Champaign.
• Guided Tour – A comprehensive guide to topic modeling with many links by Scott Weingart of Carnegie Mellon University.
• MALLET – Website for downloading and installing Mallet, an open-source and Java-based Latent Dirichlet allocation (LDA) package.
  • Topic Modeling Tutorial – Tutorial by Shawn Graham, Scott Weingart, and Ian Milligan’s on setting up a command line environment for using MALLET.
  • Mallet R Package – Ben Schmidt’s wrapping MALLET
  • GUI Tools that use MALLET
    • Google’s Topic Modeling Tool – A graphical user interface for doing topic modeling.
    • Serendip – A system for visualizing topic models by Eric Alexander and Joe Kholmann of the University of Wisconsin-Madison.
• Topic Modeling Toolbox – An alternative to MALLET for LDA topic modeling from Stanford University.

Word Embedding Models

• Women Writers Vector Toolkit – Interface for querying terms in word2vec models trained on Women Writers Project corpus.
• Vector Space Models for the Digital Humanities – Blog post by Ben Schmidt which links to his R package wrapping word2vec (word2vec is written in C).

Other Text Analysis Tools and Resources

• Voyant Tools – A simple, yet powerful, web-based text analysis and visualization tool.
• Lexos – A tool for scrubbing, chunking, and tokenizing text, in addition to performing modest analysis and visualizing clusters.
  • How to Create Topic Clouds with Lexos – Blog post by Scott Kleinman on using Lexos for topic modeling word clouds.
• AntConc – A GUI of concordancing and text analysis toolkit created by Laurence Anthony.
• CasualConc – A Mac OSX-native toolkit (AntConc’s Mac version is ported from the PC, and has some bugs).
• TextPlot – A Python package by David McClure that produces force-directed network of words in a text, the nodes of which are clustered using estimated kernel densities.
  • (Mental)Maps of Text – Blog post explaining the concept of TextPlot.
  • Textplot Refresh – Python 3, PyPi, CLI App – Blog post on downloading and setting up TextPlot.
  • Literary MRIs (or, tuning Textplot) – Blog post on TextPlot’s parameters.
• Bookworm – A customizable corpus trend visualization tool.
• Word Tree – A tool that creates word trees from a block of text.
• Applied Text Analysis Course – Material from Justin Grimmer’s course on Applied Text Analysis for Social Scientists.
• Stanford CS224N: NLP with Deep Learning – Video lectures from Stanford University’s Natural Language Processing with Deep Learning from Winter 2019.