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 85 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 10877085 words long. [0] Each item in your study carrel is, on average, 127966.0 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 84.0. [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, well, mr, know, little, time, sir, see, lady, good, never, say, like, come, must, much, old, think, mrs, may, father. [3] Using the three most frequent words, the three files containing all of those words the most are ./txt/mdp.39015030326832.txt, ./txt/uva.x030576777.txt, and ./txt/mdp.39015030326816.txt.

The most frequent two-word phrases (bigrams) include: young man, dont know, let us, old man, said mrs, said mr, said lady, every thing, lady cecilia, sir said, sir robert, young lady, every one, lady davenant, willy reilly, great deal, one day, fashionable life, lady delacour, good deal, will never, dont think, sir thomas, last night, im sure, and the three file that use all of the three most frequent phrases are ./txt/mdp.39015030326832.txt ./txt/uva.x030576777.txt, and ./txt/mdp.39015030326816.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: said, little, old, came, man, looks, ings, away, goodness, poor, mrs, young, ladies, great, sir, days, half, news, greatness, having, chapter, shall, yes, thing, eye. 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:

his poor hut it is a shabby doublet that said spare taking up the garment and seeing that 
 that it was done by his interest for he said he knew that walsingham had such nice notion
cordingly putting his hand to his hat he said with a respectful manner pray sir are you th
the cruel alterna ble necessity for your said rosamond in allow that m de tour he such a s
 up with a golden fiddle in his hand and said thats my story prince tkhisewits ireturn e p
 and when the air did clear and cool a little there was just one face that remained im pas
no kourds even in that moments agony she said heard is t by far euphrates speak as jack re
adyship to guess this time if you please said belinda you will neither of you guess till d
siege for half a year he told us that he said to them what was perfectly true that he had 
hunt through the shebeen shops for its little his ould mother sees iv him the latter part 
he other by a due severity of regimen ay said lord grey but what avails prescribing a cour
y and faced me frowning now look here he said gruffly are you a classical housemaid his to
 the same time to see paolo and nannetta said giacomo before i go up stairs how are they a
 at the end of it all said rory for what said de lacy bekase it is what was to be demonsth
ht of her after beginning with various little mock defences avertings of the head and twis
 me a pound of tobacco but my good woman said the general do not you know that dreams alwa
e now in stead of running out to meet me said he for i am pot clear that i have a farthing
lady with her breach of promise she will said he to himself be sufficiently punished by th
rudence your ladyship reproached me once said she in a layful tone for my provoking want o
some few remem beredtheir own misdeeds little boys became aware of the nefarious contents 
foolish tongue cant youdont mention my little godson for your life the little boy creeps i
gh odonnell and i know i am not wrong he said with assurance a truce to this illtimed disc
clasping it in his arms like the rest he said for ever ancwor ever and for ever bridgetbut
was a mob of his own making i dare swear said sentrey the fellow is quite equal to such a 
 at his usual slingtrot pace is that tom said he tall tom hicco hicco replied tom quite gr

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/aeu.ark:-13960-t6g171786.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/nyp.33433074931258.txt, ./txt/mdp.39015030326832.txt, and ./txt/nyp.33433074950597.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 little old - ./txt/nyp.33433074940069.txt
  2. said man sir - ./txt/mdp.39015030326832.txt
  3. said lady helen - ./txt/mdp.39015004230838.txt
  4. said mr lady - ./txt/nyp.33433074950555.txt
  5. said lady lord - ./txt/pst.000006711108.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, lady, mr., sir, day, hand, father, thing, eye, mrs., way, life, heart, house, friend, word, *, miss, lord, face, room, mother, night, woman) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, have, say, do, know, go, see, come, make, think, take, look, give, tell, hear, get, find, feel, leave, speak, ask, let, seem, stand, turn) 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., Lady, Miss, Sir, Lord, God, Helen, John, Father, Castle, Cecilia, Ireland, Mary, Frank, Harry, Owen, Herbert, Amanda, Captain, London, Nora, Dr., England, General) 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, they, her, we, them, us, himself, myself, herself, yourself, ye, themselves, one, itself, mine, yours, ourselves, hers, thim) 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, own, old, other, great, young, more, poor, such, last, dear, much, many, same, sure, first, few, long, bad, whole, full, happy, true, new) and adverbs (not, so, now, then, up, never, very, out, as, only, here, well, too, more, ever, down, again, there, even, just, still, away, back, most, all) 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.