Text Analysis & Text Mining

Open source statistical software that enables data manipulation, calculation, and graphical display. R Studio is a user-friendly graphical interface for using R. See the Wellesley R guide to get started with R.

Web-based tool for transforming, analyzing, and visualizing texts.

Free Chrome browser extension for extracting data from websites

A web-scraping tool that enables you to extract data from websites using Python.

Free, open-source tool for exploring, cleaning, and transforming data.

Interactive software for cleaning historical corpora, especially for dealing with spelling variation in Early Modern English texts.

Web-based tool for text reading, basic quantitative analysis, and visualization. Allows you to upload your own text or collection of texts and visualize patterns in a variety of ways. You can also work with existing collections of texts (Shakespeare, Austen).

Open-source text analysis software for examining word frequency and where and how words occur in texts.

An open-source visualization tool for exploring graphs and networks

Open-source Java-based tools that can be used for document classification, clustering, topic modeling, and other natural language processing.

A platform for building Python programs to work with human language data. Provides easy-to-use interfaces to over 50 corpora and lexical resources, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning.

A simple text analysis tool that creates word clouds from text that you provide.

Simple web-based text analysis tool that generates statistics about your text by analyzing word groups, keyword density, the prominence of a word or expression, and word count.

A large collection of tools, with reviews and recommendations. Browse or do a keyword search and narrow with filters.

List of tools and tutorials with brief descriptions

A great place to explore methods and techniques of text analysis. A wide range of "recipes" and tutorials walk users through a variety of tools and analytic tasks. Backgrounders provide information on aspects of text analysis.

Tutorials on a range of text analysis tools and techniques including web scraping, cleaning and transforming data, sentiment analysis, stylometry, basic text processing in R, text mining in Python, and topic modeling with Mallet.

A series of tutorials covering basic procedures in quantitative text analysis. The tutorials cover the preparation of a text corpus for analysis and the exploration of a collection of texts using topic models and machine learning. Includes sample datasets of British and French literary texts.

A short guide to finding and preparing electronic texts for analysis.

An open-access version of the 2017 O'Reilly book, this website offers an introduction to text mining using the tidytext package and other tidy tools in R.

This walk-through of a project using text analysis tools in R provides a nice overview of creating and cleaning a corpus, analyzing and visualizing word frequency, and performing sentiment analysis.

A succinct introduction to basic concepts and tools for topic modeling, with links to further resources.

Aims to provide a jargon-free introduction to the computer science concepts behind topic modeling.

Tutorial from The Programming Historian. (Also available in Spanish & French)

Sample scripts, list of scraping tools, and tutorials. From U of Texas Libraries.

A basic introduction geared toward digital humanists and focused primarily on conceptual issues. Assumes little or no background in networks or math. First in a series of blog posts by Scott Weingart from Carnegie Mellon Libraries. The whole series is available here (in reverse chronological order).