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 48 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 272927 words long. [0] Each item in your study carrel is, on average, 5686.0 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.


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 77.0. [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, son, one, said, man, now, ulysses, men, jove, went, us, achaeans, trojans, shall, go, may, come, house, let, ships, hector, came, great, back, even. [3] Using the three most frequent words, the three files containing all of those words the most are ./txt/homer-iliad-850_05.txt, ./txt/homer-iliad-850_23.txt, and ./txt/homer-odyssey-850_04.txt.

The most frequent two-word phrases (bigrams) include: one another, let us, father jove, old man, will give, went back, every one, will tell, every man, king agamemnon, ajax son, noble son, will go, go back, one else, will make, now therefore, phoebus apollo, bearing jove, will find, will take, even though, aegis bearing, now however, never yet, and the three file that use all of the three most frequent phrases are ./txt/homer-iliad-850_05.txt ./txt/homer-iliad-850_23.txt, and ./txt/homer-iliad-850_14.txt.

While often deemed superficial or sophomoric, rudimentary frequencies and their associated "word clouds" can be quite insightful:




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: sons, ulysses, great, hector, achilles, jove, said, telemachus, shall, man, men, minerva, ajax, went, agamemnon, god, juno, menelaus, aeneas, ajaxes, alcinous, apollo, came, diomed, eumaeus. And now word clouds really begin to shine:


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:

angry with you thus spoke minerva and ulysses obeyed her gladly then minerva assumed the f
 hands and nearly died of laughter as ulysses caught hold of him by the foot and dragged h
re is one here who will lay it for me ulysses went back to his own place and eumaeus strew
phitus had not been taken with him by ulysses when he sailed for troy he had used it so lo
us knew me and spoke piteously saying ulysses noble son of laertes what deed of daring wil
 assumed the voice and form of mentor ulysses was glad when he saw her and said mentor len
ework then i said to them sweethearts ulysses is indeed dead still do not press me to marr
e the son of an oak or of a rock then ulysses answered madam wife of ulysses since you per
 round his altar and at the same time ulysses woke up once more upon his own soil he had b
as they were told and set food before ulysses who ate and drank ravenously for it was long
m i wish i were as certain of finding ulysses returned when i get back to ithaca that i mi
l have better times by and by to this ulysses answered amphinomus you seem to be a man of 
to the bath woman or to some other of ulysses servants as he spoke he picked up a heifer s
have gone down upon their mourning if ulysses had not checked them and said cease your wee
etting close up to us so one of dolius s sons went as he was bid standing on the threshold
 old man sped them then he called to his sons upbraiding helenus paris noble agathon pammo
alt out to the men man by man he gave ulysses some slices cut lengthways down the loin as 
 two hence it was in crete that i saw ulysses and showed him hospitality for the winds too
 the end he deemed it best to embrace ulysses knees so he laid his lyre on the ground betw
 his eyes were closed in darkness of the sons of nestor one antilochus speared atymnius dr
ouse when alcinous heard this he took ulysses by the hand raised him from the hearth and b
xes and the son of tydeus and sixthly ulysses peer of gods in counsel but menelaus came of
was one lying by my side who was like ulysses as he was when he went away with his host an
time and would have strung it had not ulysses made a sign to check him in spite of all his
leases them as he spoke he went up to ulysses and saluted him with his right hand good day

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 son, and ./txt/homer-odyssey-850_24.txt is most about that word.

If your study carrel could be summed up in three words ("topics") then those words might be: son, ulysses, and son. And the respective files would be: ./txt/homer-iliad-850_16.txt, ./txt/homer-odyssey-850_19.txt, and ./txt/homer-iliad-850_01.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. son trojans achaeans - ./txt/homer-iliad-850_16.txt
  2. ulysses said son - ./txt/homer-odyssey-850_24.txt
  3. ulysses said house - ./txt/homer-odyssey-850_17.txt
  4. men ship said - ./txt/homer-odyssey-850_09.txt
  5. socus cyanus begins - ./txt/homer-iliad-850_08.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 (son, man, ship, hand, god, achaeans, trojans, house, ulyss, jove, spear, hector, father, achilles, horse, people, way, heaven, sea, city, day, minerva, king, one, heart) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, have, do, go, say, come, take, make, see, give, let, tell, get, speak, fall, leave, bring, kill, fight, set, know, hear, stand, answer, 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:



Proper nouns & pronouns

An extraction of proper nouns (Achaeans, Trojans, Jove, Hector, Achilles, Minerva, heaven, Agamemnon, Telemachus, Menelaus, Priam, Patroclus, Ulysses, Apollo, Atreus, Troy, Ajax, Peleus, Argives, King, Mars, Juno, Nestor, Neptune, Danaans) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (he, you, i, him, it, they, them, me, she, we, us, her, himself, myself, yourself, themselves, herself, itself, one, ourselves, mine, you--, yours, ours, 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


Adjectives & adverbs

Learning about a corpus's adjectives (own, good, great, other, old, many, brave, strong, full, such, much, dead, noble, fair, young, mighty, high, long, more, whole, dear, angry, first, bad, alive) and adverbs (not, then, so, now, up, even, out, down, thus, back, here, still, therefore, on, as, again, off, also, away, yet, once, there, never, home, 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?"




[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.