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 4 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 711056 words long. [0] Each item in your study carrel is, on average, 177764.0 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 81.0. [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, now, one, little, like, mr, never, well, know, must, yet, miss, see, thought, time, much, good, still, think, long, come, day, made, mrs. [3] Using the three most frequent words, the three files containing all of those words the most are ./txt/uc1.b5300985.txt, ./txt/hvd.hn1sbm.txt, and ./txt/pst.000012111633.txt.

The most frequent two-word phrases (bigrams) include: mr rochester, jane eyre, miss keeldar, mr moore, mrs pryor, dr john, st john, madame beck, mrs fairfax, mrs bretton, mr yorke, mr helstone, one day, dont know, just now, miss helstone, ere long, mrs reed, dr bretton, de bassompierre, mr hall, let us, next day, dare say, last night, and the three file that use all of the three most frequent phrases are ./txt/hvd.hn1sbm.txt ./txt/uc1.b5300985.txt, and ./txt/pst.000012111633.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, blackness, feels, half, having, ings, man, old, rooms, said, voices, yes, came, hearts, life, little, looks, lovely, nights, opens, self, soon, thoughtful, tioned, young. 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:

elstones pony and the two gentlemen rode away full gallop to seek surgical aid in differen
hen she was gone and now he added i must away for sweeting is off to see his mother and th
ce on my sufferingsthink of me he turned away he threw himself on his face on the sofa oh 
im and his huntress i too might have got away but i preferred to meet madame openly though
 i can never reach it it is much too far away and were it within a yard of me what would i
t all crotchets out of your head and run away and amuse yourself what with my doll asked c
o the flesh thinking they are hammering away at insensate stone he reached church early n
wn belowor i could not have felt it pass away from me under my hand and seen it vanish beh
in the basin of blood and water and wipe away the trickling gore i must see the light of t
 detained her a moment as she was moving away i want something said i what asked zoraiide 
 if he were to leave villette and go far away i should be sorry and i dropped corneille an
t she came and also the morning she went away eather a peculiar child was she not i wonder
h still lingered to show the velvet blackness of his closeshorn head and the sallow ivory 
ng vacation pauvrette said he and turned away and left the alley the examination passed ov
sa proud and contented kiss and sent him away comforted yet i saw him the next day laid on
is tance but i would not be driven quite away till i saw the event this was finally fortun
e the fear of seeming singular scared me away but by degrees as people became accustomed t
out her that it brings with it it passes away introduction xxiii like a warm day in winter
hool room at dark i allowed adele to put away books and work nd to run downstairs for from
 of an unknown tongue ere long he turned away abruptly as if baffled the professor and lef
enetrated the sable folds and waved them away then not a moon but a white human form shone
ise to keep quiet and not begin throwing away your money what a great deal you have shirle
lies in marrying a rich widow or running away with an heiress i leave such shifts to be pu
e folded up his temporary bed and put it away in my country which is my country why france
carried them into the house and put them away in the storeroom then i repaired to the libr

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/pst.000012111633.txt is most about that word.

If your study carrel could be summed up in three words ("topics") then those words might be: said, shirley, and said. And the respective files would be: ./txt/pst.000012111633.txt, ./txt/uc1.b5300985.txt, and ./txt/hvd.hn1sbm.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 did - ./txt/pst.000012111633.txt
  2. shirley mr moore - ./txt/uc1.b5300985.txt
  3. said mr jane - ./txt/hvd.hn1sbm.txt
  4. husk cheers distresses - ./txt/uva.x000094791.txt
  5. husk cheers distresses - ./txt/uva.x000094791.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 (mr., eye, day, time, hand, miss, man, night, shirley, room, mrs., heart, face, door, word, life, house, thing, way, moore, nothing, jane, caroline, lady, woman) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, have, do, say, see, know, think, go, come, make, look, take, give, seem, feel, hear, speak, tell, leave, sit, ask, find, get, like, stand) 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., Miss, Mrs., Moore, Madame, Caroline, John, M., PROFESSOR, Jane, Shirley, Rochester, God, Robert, Helstone, Dr., Bretton, Keeldar, Graham, Yorke, Lucy, JANE, Paul, St., EYRE) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (i, you, it, she, he, me, her, him, they, them, we, myself, us, himself, herself, yourself, mine, ‘, one, themselves, itself, yours, hers, ye, ourselves) 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, such, other, more, old, last, long, young, great, much, first, same, certain, white, strange, dark, full, many, new, large, few, least, quiet) and adverbs (not, so, now, then, very, up, never, out, too, only, well, as, still, more, down, again, once, here, yet, there, just, quite, ever, rather, even) 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.