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 12 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 611805 words long. [0] Each item in your study carrel is, on average, 50984.0 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 86.0. [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, king, one, will, came, went, now, man, great, son, men, come, day, time, go, saw, finn, well, made, back, three, like, two, see, shall. [3] Using the three most frequent words, the three files containing all of those words the most are ./txt/wu.89017100926.txt, ./txt/wu.89017100934.txt, and ./txt/umn.31951002408336w.txt.

The most frequent two-word phrases (bigrams) include: fairy tales, celtic fairy, one day, red branch, high deeds, let us, young man, kings son, old man, birds said, next day, st patrick, high king, chief king, emain macha, went away, every one, black horse, white swans, go back, king conor, came back, old woman, bove derg, hundred years, and the three file that use all of the three most frequent phrases are ./txt/wu.89017100926.txt ./txt/wu.89017100934.txt, and ./txt/umn.31951002408336w.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, came, thy, away, thou, went, kings, men, greatness, little, old, shall, sons, thee, comes, goods, great, likeness, days, fergus, irish, king, likely, man, son. 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:

w one language but i know many languages said the crow for i know what people say and i kn
black horse you rider of the black horse said the prince i lay you under crosses and under
e house till i find them i will help you said smallhead she came down went to the smiths h
e now awhile i cannot go just now how so said the king surprised because the boycorps have
s a man out of erin and moreover that he came from concobar and that his coming boded no g
ne and watched for the maidens when they came ashore he offered his bread as before and th
ours in the eyes of the law we know that said the little baron and lady sabrina and myself
at it availed nothing to follow her then said powel o maiden for the sake of him who thou 
ared before them good health to you king said he good health to you good man said the king
e entrance the boat that moved of itself came to the the boy who knew what the birds said 
attlefields and as his eyes unclosed he said halfdreaming still not for the queen and not
aid brian draw sword and fight for them said iuchar and iucharba and if we are the strong
never tarried and it was evening when he came in sight of the avon dia ere he reached the 
oting the earth among them now wherefore said brian i deem that he is no friend to us if s
yourself who prompted him not so in deed said caff a mad boy what made you then deceive me
without deathless courage a king is dead said ald and without sleepless eyes a king is a s
nd then you will have two sons well then said conall on condition that thou dost that i wi
men came running quickly towards him and said a stranger is approaching us from the west w
er it was that he was invited or that he came of himself the soothsayer came to the house 
ght better after that and with that they came to the ford and cuchulain drew up upon the n
nd windygap send me my right ful tribute said the king to them there isnt much we can send
came to be a flock of birds too and they came to the mountaintop and lighted on the ground
s heard over all the yard king of dublin said tom ive quarter your daughter and the king d
his holy man the most learned of all who came or shall come of the men of erin for ever fl
 men call the wren the king of the birds said the little wrens i will tell you said the mo

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/nyp.33433086985888.txt is most about that word.

If your study carrel could be summed up in three words ("topics") then those words might be: king, said, and said. And the respective files would be: ./txt/uiug.30112046521990.txt, ./txt/wu.89017100934.txt, and ./txt/nyp.33433086985888.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. king said cuchulain - ./txt/nc01.ark:-13960-t0dv2nn46.txt
  2. said king went - ./txt/wu.89017100934.txt
  3. said king man - ./txt/nyp.33433086985888.txt
  4. finn great man - ./txt/mdp.39015030708088.txt
  5. said king birds - ./txt/nc01.ark:-13960-t7jq21k0q.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 (king, man, son, day, time, finn, hand, cuchulain, house, head, night, land, place, year, child, woman, story, fairy, sea, horse, tale, father, eye, erin, thing) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, have, say, come, go, do, see, take, make, give, know, tell, thou, hear, get, call, put, find, bring, look, ask, think, stand, let, leave) 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 (King, Cuchulain, Finn, Erin, Ireland, Ulster, Fergus, Cormac, Fairy, Meave, Tales, Conor, Lir, Red, Brian, Celtic, Tara, Deirdre, FINN, Cuculain, Mac, Queen, St., Darthool, Leinster) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (he, i, it, they, him, you, she, them, me, we, her, thee, us, himself, ye, myself, themselves, herself, yourself, one, itself, thyself, mine, ourselves, hers) 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 (great, good, other, old, little, own, young, many, long, last, white, more, high, same, first, such, fair, full, black, much, wild, noble, strong, red, next) and adverbs (not, then, so, now, up, out, there, down, again, away, back, here, never, only, as, ever, well, more, very, off, even, still, far, too, together) 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.