About your study carrel

This page outlines the breadth & depth of your "study carrel" -- the results & analysis of your Distant Reader submission. Peruse the content of this page, and then consider learning how to dig deeper by reading the Distant Reader Study Carrel Cookbook. If you want "just the facts", then consider reading this text's synopsis.

Size & scope

First, the simple things. Your study carrel was created through the submission of a [SINGLE URL|FILE OF URLS|FILE FROM YOUR COMPUTER|ZIP FILE]. This ultimately resulted in a collection of 460 item(s). The original versions of these items have been saved in a cache, and each of them have been transformed & saved as a set of plain text files. All of the following analysis has been done against these plain text files.

Your study carrel is 38374038 words long. [0] Each item in your study carrel is, on average, 83422.0 words long. [1] If you dig deeper, then you might want to save yourself some time by reading a shorter item. On the other hand, if your desire is for more detail, then you might consider reading a longer item. The following histograms and box plots illustrate the overall size of your study carrel.

Readability

On a scale from 0 to 100, where 0 is very difficult and 100 is very easy, your documents have an average readability score of 87.0. [2] Consequently, if you want to read something more simplistic, then consider a document with a higher score. If you want something more specialized, then consider something with a lower score. The following histograms and box plots illustrate the overall readability of your study carrel.

Word frequencies

By merely counting & tabulating the frequency of individual words or phrases, you can begin to get an understanding of your carrel's "aboutness". Excluding "stop words", some of the more frequent words include: said, one, will, now, man, know, little, like, mr, see, time, well, dont, come, never, think, go, eyes, back, must, came, mrs, made, much, say. [3] Using the three most frequent words, the three files containing all of those words the most are ./txt/dh-crc-nd-edu-3842.txt, ./txt/dh-crc-nd-edu-3614.txt, and ./txt/dh-crc-nd-edu-3837.txt.

The most frequent two-word phrases (bigrams) include: dont know, young man, said mrs, old man, new york, first time, dont think, come back, said mr, dont want, let us, im going, last night, go back, one thing, great deal, came back, every one, one day, long time, must go, young lady, good deal, ive got, oh yes, and the three file that use all of the three most frequent phrases are ./txt/dh-crc-nd-edu-9917.txt ./txt/dh-crc-nd-edu-6772.txt, and ./txt/dh-crc-nd-edu-3842.txt.

While often deemed superficial or sophomoric, rudimentary frequencies and their associated "word clouds" can be quite insightful:


unigrams

bigrams

Keywords

Sets of keywords -- statistically significant words -- can be enumerated by comparing the relative frequency of words with the number of times the words appear in an entire corpus. Some of the most statistically significant keywords in your study carrel include: little, said, look, like, looked, looking, looks, liked, likely, good, old, man, eye, eyes, likes, away, goodness, thing, things, great, came, hand, hands, long, comes. And now word clouds really begin to shine:


keywords

Through the use of a concordance -- a keyword-in-context tool, or a "poor man's search engine" -- you can see how words are used in relation to other words. Here is a random sample of concordance entries using the two most significant keyword as input:

ussion was so novel to her i suppose she said one ought to aim at a good appearance accord
sy in her heart as she remembered that little wooden cross he would be obliged to pass wou
romptly over her shoulder well then dear said miss harbord calmly you ought to women leave
idnt understand his exhilaration when he said that but except when he reproached her for t
 within himself these are great larks he said great larks while they lastbut whats the goo
her say after the two men had had some little conversation concerning a book or two lying 
her instantly i dare say i have been a little over careful i dont knowi had a sort of pres
leasant for miss saville was a prickly little person and upon occasion would snap him up i
 by a piper called lauchlan campbell she said looking up at me suddenly it was my fault po
er had not thought within him old man he said with a new ring in his voice you must follow
ghed of course i know all about that she said poor freddie wouldnt talk about anything els
ford for ten years said lucilla with a little natural sadness and if any one were to exami
 deep blush turned towards mrs smith and said something trivial about the weather and the 
 he felt we will send back for you leila said her aunt no i want to walk there with john t
ould not go through the stitches of mine said rotha looking up it would tear every time ho
 i could scarcely wait wont you sit down said her hostess just a trifle shyly bidiane drop
 face was enthusiastic youre a trump she said extending the willing arms that were his rew
d retorted gertie she seized my hand and said oh miss gertrude that is very kind of you in
ill help you cried ruth not with those little hands replied the man vehemently you just st
brought to a terrible passnot as so many said and do say by the idolatry of popery or by t
yed guess hes too shaky to shave himself said josiah ill come about halfpast eight as josi
er jemima giggled as she picked up the little muddy boots law miss bet she said reprovingl
ntents of the woodbox empty of course he said with a smile the household is quite evidentl
it came from a drunken mans mouth that little beauty you used to travel with on the north 
amach ayont that i hope theres no danger said the marquis nane at i ken o no water at the 

Topic modeling

Topic modeling is another popular approach to connoting the aboutness of a corpus. If your study carrel could be summed up in a single word, then that word might be said, and ./txt/dh-crc-nd-edu-8943.txt is most about that word.

If your study carrel could be summed up in three words ("topics") then those words might be: said, said, and said. And the respective files would be: ./txt/dh-crc-nd-edu-5758.txt, ./txt/dh-crc-nd-edu-3842.txt, and ./txt/dh-crc-nd-edu-3487.txt.

If your study carrel could be summed up in five topics, and each topic were each denoted with three words, then those topics and their most significantly associated files would be:

  1. said like little - ./txt/dh-crc-nd-edu-6205.txt
  2. said mr did - ./txt/dh-crc-nd-edu-5852.txt
  3. said little know - ./txt/dh-crc-nd-edu-8082.txt
  4. said man like - ./txt/dh-crc-nd-edu-5058.txt
  5. said like man - ./txt/dh-crc-nd-edu-3487.txt

Moreover, the totality of the study carrel's aboutness, can be visualized with the following pie chart:

Nouns & verbs

Through an analysis of your study carrel's parts-of-speech, you are able to answer question beyonds aboutness. For example, a list of the most frequent nouns (man, time, eye, hand, day, mr., thing, way, face, _, mrs., woman, room, miss, girl, life, house, word, lady, moment, nothing, night, head, father, something) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, have, do, say, go, know, come, see, think, make, look, take, tell, get, give, _, ask, find, seem, feel, want, leave, hear, turn, speak) helps you learn what actions take place in a text or what the things in the text do. Very frequently, the most common lemmatized verbs are "be", "have", and "do"; the more interesting verbs usually occur further down the list of frequencies:


nouns

verbs

Proper nouns & pronouns

An extraction of proper nouns (Mr., Mrs., Miss, Lady, John, Sir, God, Mary, Lord, George, Mr, London, Jack, Captain, _, New, Aunt, Jane, Harry, St., Madame, Peter, York, Jim, England) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (i, he, you, it, she, him, me, her, they, we, them, himself, us, herself, myself, one, yourself, themselves, itself, ye, hers, mine, ''em, yours, ''s) enables you to answer at least two questions: 1) "What, if any, is the overall gender of my study carrel?", and 2) "To what degree are the texts in my study carrel self-centered versus inclusive?" Below are words cloud of your study carrel's proper & personal pronouns.


proper nouns

pronouns

Adjectives & adverbs

Learning about a corpus's adjectives (good, little, old, other, own, young, great, more, last, first, much, such, long, few, same, many, sure, poor, new, white, dear, bad, right, small, next) and adverbs (not, so, then, up, now, very, out, never, only, again, here, down, as, back, too, just, away, there, more, even, well, still, all, on, ever) helps you answer how questions: "How are things described and how are things done?" An analysis of adjectives and adverbs also points to a corpus's overall sentiment. "In general, is my study carrel positive or negative?"


adjectives

adverbs

Notes

[0] Once upon a time, a corpus of a million words was deemed large.

[1] To put this into context, the typical scholarly journal article is about [NUMBER] words long, Shakespeare's Hamlet is [NUMBER] words long, and the Bible is [NUMBER] words long.

[2] In this case, a Flesch readability score is being calculated. It is based on things like the number of words in a document, the lengths of the words, the number of sentences, the lengths on the sentences, etc. In general children's stories are have lower Flesch scores while insurance documents and doctoral dissertations have higher scores.

[3] "Stop words" are sometimes called "function words", and they are words which carry little or no meaning. Every language has stop words, and in English they include but are not limited to "the", "a", "an", etc. A single set of stop words has been used through out the analysis of your collection.

[4] Concordances are one of the oldest forms of text mining, first developed in the 13th century to "read" religious documents.

[6] An unsupervised machine learning process, topic modeling is a very popular text mining operation. Assuming that a word is known by the company it keeps, topic modeling identifies sets of keywords denoted by their centrality in the text. Words which are both frequent as well as in close proximity to each other are considered significant.


Eric Lease Morgan <emorgan@nd.edu>
June 11, 2019