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 144 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 9023382 words long. [0] Each item in your study carrel is, on average, 62662.0 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 82.0. [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, little, now, man, like, know, time, mrs, love, well, see, never, must, life, dont, good, may, think, much, go, come, made, mr. [3] Using the three most frequent words, the three files containing all of those words the most are ./txt/dh-crc-nd-edu-2332.txt, ./txt/dh-crc-nd-edu-3984.txt, and ./txt/dh-crc-nd-edu-7634.txt.

The most frequent two-word phrases (bigrams) include: dont know, young man, new york, said mrs, let us, conjugial love, dont think, lady byron, first time, dont want, every one, great deal, one day, come back, every day, years ago, lord byron, one thing, good deal, oh yes, old man, im sure, go back, young men, said mr, and the three file that use all of the three most frequent phrases are ./txt/dh-crc-nd-edu-9856.txt ./txt/dh-crc-nd-edu-7560.txt, and ./txt/dh-crc-nd-edu-5830.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, old, mrs, came, goodness, away, man, looks, yes, young, great, life, thing, woman, eye, news, things, greatness, goods, days, likes, longed, half, loves. 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:

ement to obey the summons come to supper said his wife coldly but he did not stir aint you
g things are the condition of life the little things can only be built up on them one must
of absence has nearly expired his father said inquiringly yes sir so nearly that i should 
 gently serene not despondingly calm she said mrs jameson again came to florence and there
 am sure maam i was not aware that i had said anything amiss said cousin tremnell huffily 
k and said as little as possible everard said miss entwhistle stroking gently is he coming
stonished how could you go on after this said i my dear why did you not return to your fat
 praise within thy house forever forever said david forever it is not death but lifeeverla
 bear all that you can shift on to me he said because otherwise you see my coming here wil
ater attraction play something marie she said suddenly but marie shook her head i dont fee
ll for naught stage and passenger eyes little lace bonnet and all had vanished away as wil
s and bands whence came your feet dear little things from the same box as the cherubs wing
 his compliments to lady whatshername he said and regrets on inspection that he is unable 
soon a charming one at st jeansurmer a little village near beaulieu was taken for the seas
of the anchor ah goodmorning daughter he said with a smile as she reached his side you are
 ronald said mrs travilla he is a dear little fellow and i hope will grow up in a way to d
reased boys who marry young derive but little enjoyment from the connubial state they are 
e church who stopped her to look as they said upon her sweet face once more and list to he
rison it appears not for the present she said no said minnie for where can we be so well a
xiv philip said shew us the father jesus said unto him he that seeth me seeth the father h
ve heard about him yet you believe him i said yes the man is extremely sensitive and almos
yes ah that is he coming up the path she said he must have caught the earlier train shall 
o be allowed to stay the night still a little less and she would have had to come to the w
ately been opened in the centre of the little towna stone building very official in appear
arylands and offered it to jim thank you said stainton lighting the cigarette he did not l

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-9081.txt is most about that word.

If your study carrel could be summed up in three words ("topics") then those words might be: said, man, and love. And the respective files would be: ./txt/dh-crc-nd-edu-2332.txt, ./txt/dh-crc-nd-edu-9081.txt, and ./txt/dh-crc-nd-edu-3984.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 like - ./txt/dh-crc-nd-edu-2332.txt
  2. said know like - ./txt/dh-crc-nd-edu-6723.txt
  3. marriage man woman - ./txt/dh-crc-nd-edu-9081.txt
  4. man like said - ./txt/dh-crc-nd-edu-3019.txt
  5. love man byron - ./txt/dh-crc-nd-edu-3984.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, _, woman, time, day, life, thing, mrs., child, love, wife, hand, way, eye, mr., mother, husband, lady, marriage, *, house, year, room, face, heart) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, have, do, say, go, know, make, see, come, _, think, take, look, give, get, tell, find, seem, feel, ask, want, leave, hear, let, 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 (Mrs., Mr., Miss, Lady, Lord, God, _, Byron, Julia, Jim, Anne, New, Sophy, England, London, heaven, Tom, Henry, Honora, Jane, King, Emmeline, Mary, John, Sir) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (i, it, he, she, you, him, her, they, me, we, them, himself, us, herself, myself, themselves, one, itself, yourself, ye, hers, mine, yours, ''em, thee) 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, other, old, own, great, more, young, such, first, many, much, last, same, new, long, dear, few, poor, happy, bad, sure, true, right, high) and adverbs (not, so, up, then, now, very, out, never, only, as, too, more, just, again, even, here, well, down, always, there, away, back, all, still, 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.