Seconding Eric's suggestion of plain text files and Voyant. There are many
many options for text mining, but Voyant is great if you're a beginner and
it provides that overarching view of the corpus that you're after. It
accepts other file types, but a collection of text files is the most
straightforward.

I'd definitely be interested to see what you find out!

----

Kayla Abner

(she/her)

*Digital Scholarship Librarian*

Digital Initiatives and Preservation

Library, Museums and Press

University of Delaware

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On Tue, May 30, 2023 at 12:48 PM Eric Lease Morgan <
[log in to unmask]> wrote:

> On May 30, 2023, at 10:46 AM, Amy Schuler <
> [log in to unmask]> wrote:
>
> > I'm interested in mining my email for phrases and concepts related to a
> > specific service that I've provided the past several years and for which
> I
> > have not kept good statistics (my bad).  Being that library & information
> > services at my organization is a one-person shop (me), text mining my own
> > email may be helpful.  I never delete email, so it's bound to be a rich
> and
> > messy corpus. I understand I may download my email archive using Google
> > Takeout, then I was thinking of using some R package like:
> >
> >   https://github.com/matthewjdenny/REmail
> >
> > Does anyone have better, easier suggestions for tools to use, or
> experience
> > they would like to share?
> >
> > --
> > Amy C. Schuler (she/her)
> > Director, Information Services & Library
> >
> > Cary Institute of Ecosystem Studies | 2801 Sharon Turnpike | Millbrook,
> NY
> > www.caryinstitute.org
>
>
> I recently finished writing a LibGuide on text mining and natural language
> processing. See:
>
>   https://libguides.library.nd.edu/text-mining-and-nlp
>
> That said, I have a hammer and to me, everything looks like a nail. See
> also:
>
>   https://reader-toolbox.readthedocs.io
>
> When doing text mining, the first thing one needs to do is ask themselves
> a research question they hope to address through text mining. The question
> can be as rudimentary as to determine the size of a collection, to denoting
> what a collection is about, to extracting a definition of social justice.
> What do you want to know?
>
> Second, you must point to content you believe can address the question. A
> set of books? A set of articles? A set of social media content? Apparently,
> the answer here is "my email".
>
> Third, you must get the content. Believe it or not, getting a set of
> digitized articles or books is more difficult than you might think. Go
> ahead. Do the perfect search against JSTOR and then download all the
> articles. Tedious, at best. The process is similar even when it comes to
> Project Gutenberg. In this case, you might already have the content, but
> you MUST convert it to plain text. When you look at raw email messages,
> they're a whole lot of noise. You will probably want to simply extract
> author, title, date, and content. Not trivial.
>
> Once you get this far, you will use any number of different techniques to
> model the text. They range from the simple to the complex:
>
>   * counting & tabulating of unigrams (words) and
>     bigrams (two-word phrases)
>
>   * counting & tabulating of parts-of-speech (nouns,
>     verbs, adjectives, etc)
>
>   * counting & tabulating of named-entities (real-world
>     things like people, places organizations, etc)
>
>   * concordancing (think ^f on steroids)
>
>   * extracting latent themes (topic modeling) and then
>     pivoting the results over time, place, authors, etc
>
>   * full-text searching complete with fields, Boolean
>     logic, relevancy ranking, etc
>
>   * semantic indexing (plotting words in an n-dimensional
>     space and calculating which words are "near" other
>     words
>
>   * extracting sentences from the text, articulating
>     grammars, and identifying sentences that match the
>     grammars such as definitions, modalities, etc.
>
>   * calculating statistically significant keywords (using
>     something similar to TF/IDF), mapping those words
>     to things in Wikidata, and discovering additional
>     relationship (meanings) through Linked Data
>
>   * identify words of interest, applying them to
>     dictionaries or thesauri (like WordNet), to again,
>     discovering additional relationships
>
>   * repeat; this is an iterative process
>
> After applying one or more of the techniques above, the student,
> researcher, or scholar ought to be able to address their research question,
> but remember, like any modeling process, the results do not necessarily
> denote truth. Instead, the results provide one with observations, and the
> observations must be placed into context though interpretation.
>
> Simple solution: Once you get plain text, feed it to Voyant Tools, and you
> will probably go a long way to addressing your question(s). [1]
>
> I have done many different things in my career, and text mining has proven
> to be one of the more satisfying. Through text mining and natural language
> processing I have been able to read huge corpora (like eight different
> editions of Encyclopedia Britannica, the whole of Chinese history, or 100
> years of United States Presidential papers). It is fun to compare and
> contrast ideas as they ebb and flow over time. It is fun to read an entire
> literature and make up my own mind what it means. For example, I assert
> Moby Dick is as much an instruction manual on whaling as it is about a
> man's relentless pursuit of the leviathan which chewed off his leg.
>
> Okay, my reply has probably been overkill, but I couldn't help myself. :)
>
>
> [1] Voyant Tool - https://voyant-tools.org/
>
> --
> Eric Lease Morgan
> Navari Family Center for Digital Scholarship
> Hesburgh Libraries
> University of Notre Dame
>
>
>
>
>
>