Distant Reader Study Carrel

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 43 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 5021271 words long. [0] Each item in your study carrel is, on average, 116774.0 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 83.0. [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: will, said, one, two, now, men, time, us, well, see, way, may, think, made, great, good, take, three, get, know, go, first, must, little, shall. [3] Using the three most frequent words, the three files containing all of those words the most are ./txt/mdp.39015073429188.txt, ./txt/mdp.39015063943040.txt, and ./txt/mdp.39015073483813.txt.

The most frequent two-word phrases (bigrams) include: crown vo, vo cloth, cloth elegant, olivine edges, fullpage illustrations, harry said, bonnie prince, will take, prince charlie, will go, let us, elegant olivine, page illustrations, next morning, gordon browne, dont think, dont know, sir john, mr beveridge, cloth extra, malcolm said, next day, hundred yards, two days, old flag, and the three file that use all of the three most frequent phrases are ./txt/mdp.39015073483813.txt ./txt/mdp.39015073480678.txt, and ./txt/mdp.39015073429212.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: away, said, little, great, men, came, young, ings, having, longing, goodness, looks, english, shall, days, manned, placing, day, goods, news, boys, captains, liking, old, rounded. 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:

 through his farm accounts oh robert she said i cant find tom and she burst into tears not
t you hand me the next rifle tim charlie said sharply it went clane out of my head altoget
n knock as long as they like and then go away thinking we are out godfrey chatted for a sh
john and me and the rest of us yes harry said but i am not talking about mis takes in gram
u again thekla gave malcolm her hand but said nothing she had now reached an age when carl
 i expect it just shot over it sam harry said it must have been travelling so mighty fast 
ngs what a rich and beautiful country he said it is very pleasant to look at de lille agre
d out by the utes or they might have got away at any rate there was no counting on them th
against colonel hol liday who had as she said encouraged him in brawl ing the colonel bent
 varley as he did last time mrs mulready said i am sure i hope he has else he will be wand
an end of it that water cask the marquis said that is an eighteengallon cask it is as much
nd i dont belong to you now sir but will said earnestly i shall always feel whatever regim
raced roof was a charming one and as tim said charlie had in the fine weather converted th
their force by sending one of the prizes away having seen them off gervaise retired to the
your worship must do as you like malcolm said dog gedly i have done my best to answer your
ut we are also longing to know the count said but i have not yet introduced you to my wife
argo i dont like losing the ship francis said of course it is only a stroke of bad fortune
to you we will not very well the officer said then your blood be on your own heads and tur
 forester rode up monsieur le marquis he said i have good news for you the demon wolf is i
es son sir he bade me fetch him straight away from the folk with whom he was living and ta
lf i did not believe it possible charlie said at last giving vent to his anger and scorn a
n port and watch this corsair fleet sail away to carry out its work of destruction by this
and mother but i cannot and will not run away and leave them all here in danger victor sai
ou are good indeed to say so mr mulready said in a tone of deep gratitude but i might have
s of course thats unfortunate the lawyer said uneasily of course they will rnako a point o

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/mdp.39015020934967.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/mdp.39015073484076.txt, ./txt/mdp.39015063943040.txt, and ./txt/mdp.39015073429238.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 time men - ./txt/mdp.39015063943040.txt
  2. said harry time - ./txt/mdp.39015073478847.txt
  3. said prince men - ./txt/mdp.39015073486956.txt
  4. men said time - ./txt/mdp.39015039579761.txt
  5. malcolm men said - ./txt/mdp.39015010944752.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, day, way, place, hand, harry, story, boy, hour, order, sir, officer, boat, life, house, father, horse, night, army, captain, side, mr., water, fire) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, have, do, say, go, take, see, come, make, get, think, know, give, tell, find, leave, hear, look, bring, keep, fall, carry, send, ask, pass) 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 (Harry, Mr., Malcolm, Sir, Crown, Charlie, England, English, Rupert, Captain, Ronald, John, France, Ned, Jeanne, Colonel, G., Horace, Gervaise, Henty, Paris, Tom, TERROR, Mrs., St.) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (i, he, it, you, they, we, them, him, me, she, us, her, himself, themselves, myself, yourself, one, itself, herself, ourselves, yours, ''em, ye, mine, ours) 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, other, great, little, young, many, few, more, first, last, own, long, full, sure, strong, old, much, able, next, large, such, small, same, high, english) and adverbs (not, up, so, out, now, then, down, as, here, well, once, again, very, there, off, only, away, back, on, soon, just, even, in, still, too) 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.