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 14 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 670240 words long. [0] Each item in your study carrel is, on average, 51557.0 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 88.0. [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, will, one, now, little, good, miss, come, know, mrs, dont, girl, go, well, like, came, nora, see, must, mother, great, never, time, went, oh. [3] Using the three most frequent words, the three files containing all of those words the most are ./txt/uva.x004763184.txt, ./txt/hvd.hn2kp1.txt, and ./txt/uc1.c036890231.txt.

The most frequent two-word phrases (bigrams) include: irish boy, said nora, said mrs, madame de, de rosier, girl graduate, sweet girl, dont know, said andy, good aunt, mrs preston, kings son, miss heath, little girl, dr campbell, lady augusta, young lady, mrs cameron, mrs oshanaghgan, come back, mademoiselle panache, mr mountague, st benets, said miss, wonderful chair, and the three file that use all of the three most frequent phrases are ./txt/uc1.c036890231.txt ./txt/hvd.hwjw3i.txt, and ./txt/hvd.hwjw3h.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, great, came, looks, man, ings, goods, mrs, old, yes, young, away, chapter, comes, eye, ladies, likeness, things, cried, dears, girls, goodness, liking, poor. 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:

hundred of it thank you for nothing then said andy independently im only a poor irish boy 
be stirred up to attack them but brendan said i wonder much at your folly why do you dread
 help our living i have been to the city said spy and could see nothing these are hard tim
the mo ment you are silly and prejudiced said mrs oshanagh gan it is a great trial to me t
iously thats what alfred asked you to do said andy coolly why didnt you do it because i di
aid off the debt i congratulate you rose said annie day yes said rosalind blushing i am gl
character of frederick the great ty rant said i to myself what can this mean the king snat
 could helen have married such a man she said to herself i never can get on with him never
ike to hear about that old womans neat little garden cried herbert and so should i said mr
red her young friend of a welcome maggie said from the other side of the locked door i am 
 you and me you can speak for yourself i said she behaved very well it is unnecessary to c
doctor patted pollys curly head child he said we have all of us to go up mountains and if 
nything in my power to please you bet he said i aint a bad sortmy barks worse nor my bite 
se she had watched it all the winter and said that she was very fond of it but that she wa
ts a girl of the people the gel yourself said granger who felt ready to dance with vexatio
 hand and wrung it he raised the white little hand to his lips and kissed it there now he 
 reparation as you term it then mr stone said mrs preston her only an irish boy thin lips 
isabella p said matilda a noise no where said isabella and went on talking alternately to 
ow that my mamma is dead bless the child said goodnatured and un forsaken suspicious nurse
ladies marvel you are pleased to flatter said fair feather but my husband has a brother th
 wish mamma would have let me sit up a little longer said favoretta for i am not at all sl
uptly bet i want yer to be good to the little boysto stand atween them and their father an
shoulders for seven times seven years he said have i carried this pannier and no one ever 
ache well pity you profess pity for them said mr dashwood and pity you know is akin to lov
l that i remember would you eat or drink said the woman of a thousand years crowfeatherclo

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/hvd.hwjw3g.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/hvd.hwjw3i.txt, ./txt/nc01.ark:-13960-t2m62kx1c.txt, and ./txt/mdp.39015066953731.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 nora miss - ./txt/nc01.ark:-13960-t2m62kx1c.txt
  2. said bet forester - ./txt/uc1.b000321912.txt
  3. said came great - ./txt/njp.32101066120815.txt
  4. polly said flower - ./txt/uva.x004763184.txt
  5. king son said - ./txt/hvd.32044086877933.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 (girl, mrs., mother, nora, man, time, day, miss, room, thing, boy, father, lady, hand, eye, maggie, way, mr., house, word, night, child, face, people, andy) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, have, say, do, go, come, see, know, think, take, make, look, give, get, tell, find, hear, want, speak, leave, put, ask, stand, let, 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 (Mrs., Nora, Miss, Maggie, Mr., Andy, Bet, Priscilla, Polly, Helen, Forester, Dr., Flower, Molly, Prissie, Godfrey, Preston, King, Squire, O''Shanaghgan, Howard, Henry, Aunt, Rosier, Dent) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (i, you, she, it, he, me, they, her, him, them, we, herself, us, himself, myself, yourself, themselves, one, hers, yours, ye, mine, ''em, ''s, itself) 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, great, young, old, own, other, poor, more, irish, dear, many, last, sure, much, long, first, such, next, small, bad, same, few, white, glad) and adverbs (not, so, now, up, very, then, out, never, only, here, as, down, back, just, away, there, well, again, more, too, ever, quite, all, polly, in) 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.