Digital Humanities Societies:

• ADHO Alliance of Digital Humanities Organizations;
• EADH: European Association for Digital Humanities;
• ACH: The Association for Computers and the Humanities.

Digital Humanities Journals:

• Digital Humanities Quarterly;
• DSH, The Journal of Digital Scholarship in the Humanities;
• Language Resources and Evaluation (Previously Computers and the Humanities).

Digital Humanities and diversity:

• Deb Verhoeven’s presentation on the ‘parade of patriarchs’, Digital Humanities Congress in Sydney 2015: “Has anyone seen a woman?”. Available on Speakola and YouTube;
• Article on cultural diversity and the Digital Humanities;
• Special issue of Digital Humanities Quarterly on Feminisms in Digital Humanities.

Tools for Text Mining and Topic Modelling:

• Information on Digital Humanities tools and methods peer reviewed and suited for starters: The Programming Historian. 82 tutorials in English, 45 tutorials are also available in Spanish and 10 tutorials are translated in French. Text analysis tutorials are referred to as ‘distant reading’ (term coined by Franco Moretti);
• Brilliant fundgrube for a lot of very interesting stuff: Quanthum (they also mention the Programming Historian);
• Voyant tools;
• AntConc Tool for concordancing;
• Lancsbox: Very useful tool that incorporates a lot of methods and knowledge on computational linguistics. Lancsbox will allow you to compare corpora and also cover POS tagging, so you will be able for instance to count verbs, nouns etc. in your corpus;
• Iramuteq: French G(raphical) U(ser) I(nterface) for text mining in R;
• Article with a survey of (some) text analysis packages for R. Published in Language Resources and Evaluation, Vol. 53, Issue 4 (December 2019);
• Further references on tools and tutorials, also on Quanthum, here and here.

Books:

•  Shawn Gram, Ian Milligan and Scott B. Weingart, Exploring big historical data: the historian’s macroscope. Pre-draft version available here.
•  Ashish Kumar, Avinash Paul, Mastering text mining with R, Packt Publishing.

Tutorials:

• Topic Modelling with Mallet: Mallet Topic Modelling on the Web;
• Mallet is also available as an R package for use in R or R studio;
• Python and Natural Language Processing.

Text mining in R:

• Introduction to text mining using R;
• Useful packages for NLP and text mining;
• How to build topic models in R.
• If you do not want to use the bag-of-words method, but use parsing and speech tagging.

Courses:

• Coursera – ‘Python for Everybody’ (free);
• Coursera – ‘Applied Text Mining in Python’ (free);
• Datacamp: Various interesting courses in text mining, sentiment analysis and social network analysis. Datacamp offers notebooks with exercises, which is a useful approach.