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 154 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 12613717 words long. [0] Each item in your study carrel is, on average, 83535.0 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: one, said, will, now, man, little, time, like, see, well, two, mr, us, know, back, made, came, come, go, old, good, way, great, much, men. [3] Using the three most frequent words, the three files containing all of those words the most are ./txt/pst.000020202286.txt, ./txt/hvd.32044092664325.txt, and ./txt/hvd.hnfjby.txt.

The most frequent two-word phrases (bigrams) include: jimmie dale, new york, old man, dont know, young man, let us, submarine boys, said mr, one day, next day, bobbsey twins, united states, every one, come back, next morning, de la, one side, long time, jack benson, first time, years ago, mr fogg, go back, im going, last night, and the three file that use all of the three most frequent phrases are ./txt/hvd.32044015457534.txt ./txt/hvd.32044015457559.txt, and ./txt/hvd.hn5p18.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, came, away, looks, ings, man, great, news, goods, goodness, men, yes, young, eye, went, having, half, shall, greatness, ing, times, manned, sir. 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:

major dunwoodie worthy of the confidence said henry wharton keenly indignant at the trifli
he two were comfortably settled in the little writingroom that opened out of mrs mallowes 
e with mrs downey after hear ing what he said to fergus and maxwell i realize nothing will
ed from the roof i am a christian calmly said a voice in the court yea i have striven to b
ethousanddollar reward story jane carson said jimmie mystified why shes my girl your girl
 realised his duties my dear baroness he said my dear lady let me wish you every happiness
times than you have scouts dont get lost said peter they get lost so they can find their w
ie dale were working surely silently a little steel instrument that was never out of posse
six little bunkers at captain bens six little bunkers at cowboy jacks the outdoor girls se
ve the lion begged flossie hush children said mr bobbsey as freddie opened his mouth to wa
o keep in touch with things suddenly she said i was once in leghorn for a few hours we wer
ords cynically at their face value no he said but you know somebody who does dale hesitate
 shed been munching i got to tell you he said soberly there is now only one chance in ten 
ive at calcutta in time nothing could be said in reply to such complete cer tainty it was 
was as cunning as any fox and answered little but wind in the midst of which tonguefence i
 some real bushes and make believe trees said mr whipple i couldnt grow real big woodland 
ittle girl seemed to need one mr bobbsey said the store owner oh that will be all right im
 and she has snooded her yellow hair a little aboon her bree and she is on to miles cross 
at young scapegrace stephen ill be bound said the man to himself evidently not dis pleased
r of the man among them and that other little troop just appear ing children with yet happ
ed they had the nervous jumps at every little noise or unexpected movement and even the na
 there was enough food left to serve a little tea around five oclock since meadow brook fa
aw flossie there really wasnt any danger said the keeper again here ill show you how gen t
of his success in the regatta was it not said that jacopo the bravo was his competitor a l
well be that i am a fellowcoun tryman he said did not the name of sabine speak to me of si

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/osu.32435052941119.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/njp.32101079666804.txt, ./txt/mdp.39015049004305.txt, and ./txt/nyp.33433075815062.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 man great - ./txt/hvd.32044092664325.txt
  2. said man like - ./txt/mdp.39076002619505.txt
  3. said little mr - ./txt/nyp.33433075815062.txt
  4. said dale jimmie - ./txt/umn.31951001003697p.txt
  5. amyas man ye - ./txt/chi.39693884.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, hand, mr., way, eye, night, thing, head, *, boy, life, face, house, place, woman, room, door, year, sir, father, word, side, foot) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, have, do, say, go, come, see, make, know, take, get, look, think, give, tell, find, leave, hear, seem, ask, turn, stand, call, let, keep) 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., Jack, Tom, God, Captain, Miss, Sir, Dale, Jimmie, New, Lord, John, England, Amyas, York, Frank, London, St., King, Benson, Paul, Dick, Peter, Lady) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (i, he, it, you, she, they, him, we, me, them, her, us, himself, myself, herself, themselves, ye, thee, itself, yourself, one, ourselves, ‘, mine, ''em) 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, great, old, more, young, own, last, many, long, such, few, same, first, much, white, small, new, large, poor, high, next, black, full) and adverbs (not, so, then, up, now, out, very, down, here, as, again, only, there, back, too, never, more, away, just, well, even, still, off, on, once) 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.