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 6 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 290111 words long. [0] Each item in your study carrel is, on average, 48352.0 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 60.0. [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, man, will, may, also, good, state, things, men, must, now, reason, others, first, people, many, power, government, different, either, two, greek, nature, every, part. [3] Using the three most frequent words, the three files containing all of those words the most are ./txt/8438.txt, ./txt/6762.txt, and ./txt/2412.txt.

The most frequent two-word phrases (bigrams) include: every one, good man, one another, practical wisdom, supreme power, imperfect selfcontrol, moral choice, one may, like manner, let us, free state, perfected selfmastery, one person, public affairs, mean state, already mentioned, already said, first place, chief good, something else, one thing, one man, take place, must necessarily, takes place, and the three file that use all of the three most frequent phrases are ./txt/8438.txt ./txt/6762.txt, and ./txt/26095.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: man, actions, calling, characters, goodness, greatness, greeks, likenesses, men, mere, natures, people, stating, thing, tragedies, word, actings, aristotle, best, called, certain, chapter, commons, councils, differed. 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:

tance man is predicated of the individual man now in this case the name of the species man
ture such then is the magnificent man the man who is in the state of excess called one of 
d as sufficient sidenote vi again since a man may do unjust acts and not yet have formed a
er still less can it be anger because actions done from anger are thought generally to be 
hing some another nay oftentimes the same man gives a different account of it for when ill
 not thought to act unjustly but here the man does to himself and suffers the same things 
fe painting also seems useful to enable a man to judge more accurately of the productions 
self about his own concerns merely is the man of practical wisdom while they who extend th
things which are hateful and mean for the man who is truly good and sensible bears all for
character and disposition unless indeed a man is acting from any special motive now since 
ear moreover in particular instances if a man knows definitely that such and such a thing 
nd of this kind are thought to be the actions according to virtue because doing what is no
 unequal sidenote b well since the unjust man is also a grasping man he will be so of cour
ute but there is no reason why the clever man should not be of imperfect selfcontrol and t
th as either virtue family beneficent actions or these joined with power for those who hav
ng very difficult if not impossible for a man to be a good judge of what he himself cannot
the highest excellence since the better a man is the more is he worth and he who is best i
t not in moral choice and next sudden actions we call voluntary but do not ascribe them to
me analogy holds good with respect to actions of which those which are of a superior natur
him of those who try to give pleasure the man who simply and disinterestedly tries to be a
what he does are at variance but the good man does what he ought to do because all intelle
lfcontrol and the imperfection of it if a man yields to pleasures or pains which are viole
se on the score of virtue if there is one man better than all the other men of worth who a
ich is common to all it may be asked if a man has abilities equal to his appointment in th
our present forms of government where one man calls another his son another calls that sam

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 man, and ./txt/2412.txt is most about that word.

If your study carrel could be summed up in three words ("topics") then those words might be: man, state, and tragedy. And the respective files would be: ./txt/8438.txt, ./txt/6762.txt, and ./txt/1974.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. man good things - ./txt/8438.txt
  2. state government power - ./txt/6762.txt
  3. tragedy poet poetry - ./txt/6763.txt
  4. council people lot - ./txt/26095.txt
  5. man case said - ./txt/2412.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, thing, state, part, one, government, reason, person, case, power, other, law, virtue, people, object, action, pleasure, nature, time, city, self, life, way, kind, instance) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, have, do, say, make, take, give, call, think, mean, seem, come, know, see, act, use, follow, arise, find, choose, live, speak, receive, establish, bring) 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 (Self, Aristotle, Control, P., Pleasure, Tragedy, Greek, Council, Moral, Friendship, Virtue, Practical, Wisdom, Happiness, Imperfect, Pleasures, Homer, Justice, Good, Reason, Socrates, Plato, Plot, Lacedaemonians, Working) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (it, they, he, we, them, him, i, themselves, himself, one, itself, us, you, ourselves, she, me, her, theirs, herself, mine, ours, thee, yourself, ''s, eudÃ) 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 (good, other, same, such, great, many, different, own, necessary, common, first, proper, bad, particular, greek, certain, more, general, true, right, public, evident, possible, moral, equal) and adverbs (not, so, also, then, now, only, more, most, again, thus, just, too, very, therefore, even, well, as, up, first, already, however, rather, yet, always, out) 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.