I'll second both Eric and Kayla, if your question is "I wonder what is in here" ... then Voyant is a good place to start.

If you aren't comfortable doing text-analysis programming and want to be, we are running the Text Analysis Pedagogy (TAP) Institute starting in July and you could come and take some classes (https://www.ithaka.org/constellate/text-analysis-pedagogy-institute/). 😊  It includes a spaCy series, which is one of the packages Eric recommends at the end of his LibGuide.

~ Amy

--
Amy J. Kirchhoff (she/her)
Constellate Sr. Manager / ITHAKA 
Twitter: @AmyPlusFour
 
Constellate is the only text and data analysis platform that integrates access to scholarly content and open educational resources into a cloud-based application and lab to help faculty, librarians, and other instructors easily teach text and data analysis.
 
Take your research further with text and data analysis skills!

-----Original Message-----
From: Code for Libraries <[log in to unmask]> On Behalf Of Kayla Abner
Sent: Tuesday, May 30, 2023 2:29 PM
To: [log in to unmask]
Subject: Re: [CODE4LIB] Text mining Gmail

>>>>>Caution: This message did not originate from within ITHAKA's email 
>>>>>system. Please use caution when opening attachments and following 
>>>>>links within this message.<<<<<

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
>
>
>
>
>
>