 |
 |
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 [log in to unmask] ***The **University of Delaware, a land grant institution, is located on land that was and continues to be vital to the web of life of the Nanticoke and Lenni-Lenape people. We express gratitude and honor the people who have inhabited, cultivated, and nourished this land for thousands of years, even after their attempted forced removal during the colonial era and early federal period*. The University of Delaware also financially benefitted from the expropriation of Indigenous territories in the region colonially known as Montana. View the full Living Land Acknowledgement <https://sites.udel.edu/antiracism-initiative/committees/american-indian-and-indigenous-relations/living-land-acknowledgement/#Living_Land_Acknowledgement> .**** [image: University of Delaware] 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 > > > > > >