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 1 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 790451 words long. [0] Each item in your study carrel is, on average, 790451.0 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 80.0. [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: shall, lord, god, said, ye, will, man, israel, son, king, people, came, house, come, one, children, also, day, land, men, shalt, let, go, hand, us. [3] Using the three most frequent words, the three files containing all of those words the most are ./txt/input-file.txt, , and .

The most frequent two-word phrases (bigrams) include: ye shall, lord god, shall come, every one, every man, lord said, let us, jesus christ, lord shall, burnt offering, right hand, shall ye, neither shall, priest shall, thine hand, lord spake, now therefore, will make, years old, shall go, will bring, shall eat, will give, meat offering, lord came, and the three file that use all of the three most frequent phrases are ./txt/input-file.txt , and .

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: accord, answering, arts, away, begat, beholding, brought, calling, came, cities, comings, concern, david, days, doth, evils, fathers, forth, gods, goodness, greatness, handed, hasting, hath, having. 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:

led with their sacrifices and jesus answering said unto them suppose ye that these galilae
ks and he was a samaritan and jesus answering said were there not ten cleansed but where a
rist unto them and the people with one accord gave heed unto those things which philip spa
 whom say the people that i am they answering said john the baptist but some say elias and
father out and intreated him and he answering said to his father lo these many years do i 
nded having the same love being of one accord of one mind let nothing be done through stri
e city which opened to them of his own accord and they went out and passed on through one 
d what buildings are here and jesus answering said unto him seest thou these great buildin
h him for she is a sinner and jesus answering said unto him simon i have somewhat to say u
were blind he gave sight then jesus answering said unto them go your way and tell john wha
hem but whom say ye that i am peter answering said the christ of god and he straitly charg
n your nets for a draught and simon answering said unto him master we have toiled all the 
nto him then certain of the scribes answering said master thou hast well said and after th
tion but being more forward of his own accord he went unto you and we have sent with him t
im out and they could not and jesus answering said o faithless and perverse generation how
 please them well in all things not answering again not purloining but shewing all good fi
ia the jews made insurrection with one accord against paul and brought him to the judgment
hey said unto him caesars and jesus answering said unto them render to caesar the things t
d their ears and ran upon him with one accord and cast him out of the city and stoned him 
the seven had her to wife and jesus answering said unto them do ye not therefore err becau
 him which had the dropsy and jesus answering spake unto the lawyers and pharisees saying 
 why cumbereth it the ground and he answering said unto him lord let it alone this year al
hee and comest thou to me and jesus answering said unto him suffer it to be so now for thu
 one of them whose name was cleopas answering said unto him art thou only a stranger in je
 was fully come they were all with one accord in one place and suddenly there came a sound

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 shall, and ./txt/input-file.txt is most about that word.

If your study carrel could be summed up in three words ("topics") then those words might be: shall, zuzims, and zuzims. And the respective files would be: ./txt/input-file.txt, ./txt/input-file.txt, and ./txt/input-file.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. shall unto lord - ./txt/input-file.txt
  2. zuzims disputations policy - ./txt/input-file.txt
  3. zuzims disputations policy - ./txt/input-file.txt
  4. zuzims disputations policy - ./txt/input-file.txt
  5. zuzims disputations policy - ./txt/input-file.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 (lord, god, man, son, king, day, israel, house, people, hath, child, hand, land, thing, father, shalt, city, word, david, name, earth, jesus, servant, brother, offering) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, say, have, thou, come, go, do, make, take, give, bring, see, let, hear, know, pass, saith, send, put, call, speak, accord, eat, set, 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 (LORD, God, Israel, Lord, David, Jesus, Moses, Judah, Jerusalem, Egypt, Christ, heaven, Behold, Saul, Jacob, Aaron, Solomon, GOD, Babylon, Son, Father, Pharaoh, Levites, Philistines, Jews) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (he, i, they, him, them, it, me, thee, ye, you, we, us, she, her, himself, themselves, thyself, myself, mine, ourselves, itself, herself, one, thy, yourselves) 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, thine, own, many, holy, other, behold, high, mine, old, young, dead, wicked, first, same, right, more, strong, mighty, full, wise, evil, whole, little) and adverbs (not, up, then, also, even, now, so, out, therefore, down, away, thereof, forth, thus, again, there, together, ever, in, off, yet, more, surely, behold, over) 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.