Text Analysis

This page is currently in development. Below there are a number of helpful resources, many of which come from Northeastern’s Lab resources and Miriam Posner’s crowdsourced “Corpra of Interest” document.

Getting Started

• Finding data sets for text analysis
• “Where to Start,” courtesy of Ted Underwood
• Stanford’s Introduction, from ‘‘Tooling Up for Digital Humanities’’
• Stanford’s “Natural Language Processing with Deep Learning”
• Julia Silge and David Robinson’s “Text Mining with R: A Tidy Approach” textbook
• Brendan O’Connor, et al., “Computational Text Analysis for Social Science”
• Duke University’s Introduction to Text Analysis libguide

Python

• Folgert Karsdorp, ‘‘Python Programming for the Humanities’’
• Charles Severance, ’’Python for Informatics”, an applied but comprehensive introductory Python text with sections on text parsing
• Download and install Python
• Download and install PyCharm, an Integrated Development Environment (IDE) for Python
• Download and install IPython, an interactive shell for Python
• The Hitchhiker’s Guide to Python
• Neal Caren’s tutorials on Python and text analysis

R (Programming Language)

• Matthew Jockers, ‘‘Text Analysis With R for Students of Literature’’ (PDF available for download via the NEU Library)
• Download and install R
• Download and install RStudio, an Integrated Development Environment (IDE) for R
• RSeek, a search tool for finding resources on R
• Simple data types in R

Topic Modeling

• JDH’s Special Issue on Topic Modeling (2012)
  • Megan R. Brett’s “Basic Introduction” (conceptual)
• Ted Underwood, “Topic modeling made just simple enough”
• Scott Weingart’s “Guided Tour” (comprehensive, lots of links)
• Ben Schmidt’s article about Latent Dirichlet allocation’s (LDA’s) limitations (also from the JDH special issue)
• MALLET, an open-source and Java-based Latent Dirichlet allocation (LDA) package
  • Shawn Graham, Scott Weingart, and Ian Milligan’s tutorial for setting up a command line environment for using MALLET
  • Ben Schmidt’s R package wrapping MALLET
  • GUI Tools that use MALLET
    • Google’s Topic Modeling Tool
    • Serendip
• Stanford Topic Modeling Toolbox (an alternative to MALLET)

Word Embedding Models

• Ben Schmidt’s Blog Post on Vector Space Models
  • Links to his R package wrapping word2vec (word2vec is written in C)