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 24 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 929704 words long. [0] Each item in your study carrel is, on average, 38738.0 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 73.0. [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: socrates, one, will, may, said, good, true, say, us, man, like, shall, must, now, another, things, also, yes, men, let, nature, first, many, two, know. [3] Using the three most frequent words, the three files containing all of those words the most are ./txt/1497.txt, ./txt/1750.txt, and ./txt/1672.txt.

The most frequent two-word phrases (bigrams) include: let us, one another, young socrates, yes socrates, true socrates, certainly socrates, just now, true stranger, first place, every one, like manner, quite true, socrates socrates, theaetetus yes, socrates yes, true athenian, every man, theaetetus certainly, theaetetus true, will make, socrates well, one thing, one will, two kinds, cleinias certainly, and the three file that use all of the three most frequent phrases are ./txt/1738.txt ./txt/1750.txt, and ./txt/1497.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: socratic, greatness, man, having, thing, true, goodness, men, plato, socrates, yes, shall, goods, naturally, natures, said, arts, great, likes, souls, certainly, forming, god, life, liked. 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:

city and country for because of the greatness of their empire many things were brought to 
 will be nothing small neither will greatness for greatness will have a greaterand there w
acteristic also of the temper of the socratic enquiry is the proposal to discuss the teach
eauty is the cause of the beautiful greatness the cause of the great smallness of the smal
ogue will find in the hippias a true socratic spirit they will compare the ion as being ak
 having regard to the relativity of greatness and smallness to each other and there is ano
e the pains exceed the pleasures in greatness and number and frequency hence one of the tw
e been lost as well as gained in the socratic dialectic he felt no incongruity in the vete
d in the former according to the old socratic notion as deferred or accumulated pleasure w
hen the one if of such a nature has greatness and smallness that appears to be true and gr
ritings of plato is a revival of the socratic question and answer applied to definition is
 are as nothing either in number or greatness in comparison with those other recompenses w
usand inhabitants with the barbaric greatness of the island of atlantis plato probably int
er be great and also small but that greatness in us or in the concrete will never admit th
ubject of discourse and also of the greatness of our sorrow he was like a father of whom w
keness the euthydemus belongs to the socratic period in which socrates is represented as w
different style framed not after the socratic but after some pythagorean model as in the c
m they set over them and nurse into greatness yes that is their way this and no other is t
re they must be of an inconceivable greatness why i said what was ever great in a short ti
he one had smallness and the others greatness or the one had greatness and the others smal
is view the phaedo is not one of the socratic dialogues of plato nor on the other hand can
 is wholly absent true but absolute greatness is only greater than absolute smallness and 
me only in the earlier dialogues the socratic conception of universals is illustrated by h
f leading athenians belonging to the socratic circle the dialogue commences with a request
ecome great because they partake of greatness and that just and beautiful things become ju

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 socrates, and ./txt/1656.txt is most about that word.

If your study carrel could be summed up in three words ("topics") then those words might be: socrates, socrates, and shall. And the respective files would be: ./txt/1672.txt, ./txt/1572.txt, and ./txt/1750.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 socrates good - ./txt/1672.txt
  2. socrates protarchus like - ./txt/1572.txt
  3. shall athenian cleinias - ./txt/1750.txt
  4. socrates theaetetus stranger - ./txt/1738.txt
  5. predict appealed denounced - ./txt/1635.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, socrate, thing, socrates, one, nature, word, soul, art, god, time, stranger, knowledge, part, state, way, law, life, name, pleasure, mind, body, other, theaetetu, plato) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, have, say, do, make, know, let, think, give, see, speak, take, go, call, come, mean, suppose, find, become, tell, ask, appear, seem, hear, reply) 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 (Socrates, Plato, CLEINIAS, God, STRANGER, Republic, Sophist, State, Phaedrus, Protagoras, Homer, Gorgias, heaven, Greek, Parmenides, Theaetetus, Stranger, Cleinias, Timaeus, Gods, Athens, Callicles, Zeus, Philebus, Euthydemus) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (he, i, you, they, we, them, him, it, us, me, himself, themselves, she, itself, myself, ourselves, her, yourself, one, herself, yours, mine, theirs, ours, ye) 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 (other, good, true, great, same, many, own, such, young, more, first, human, bad, old, right, wise, able, whole, different, high, less, general, natural, common, similar) and adverbs (not, then, only, now, also, so, very, more, as, certainly, well, most, again, just, up, therefore, yet, always, never, far, first, too, still, even, rather) 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.