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.
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 76 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 5728933 words long.  Each item in your study carrel is, on average, 75381.0 words long.  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 81.0.  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.
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, one, will, now, time, man, little, mr, like, old, well, see, know, made, much, good, two, never, come, must, us, way, day, go, came.  Using the three most frequent words, the three files containing all of those words the most are ./txt/PriMrs.xml.txt, ./txt/TalPhil.xml.txt, and ./txt/SatToll.xml.txt.
The most frequent two-word phrases (bigrams) include: new zealand, old man, young man, dont know, crown vo, said mrs, said mr, let us, one another, one day, young men, mr atherton, next day, every one, first time, dont think, good deal, next morning, come back, great deal, will go, vo cloth, long time, young lady, years ago, and the three file that use all of the three most frequent phrases are ./txt/WhiReve.xml.txt ./txt/MarAmon.xml.txt, and ./txt/WisTike.xml.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: like, likely, little, look, looking, said, looked, looks, good, great, old, liked, times, time, away, long, man, likes, goods, liking, day, days, goodness, chapter, longed. 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:
miss with the remark that they were much like the average young men found in cities they s s at our feet at length when it seemed likely that we would follow the example of those wi rymakings i must tell you that our house like most in spanish town was a large upstair one ommendation to the minister of would you like to see it sir said the poor woman i should v als without natural means of defence but like all shy things they become bolder than other und of the g the diphthong ai is sounded like long i or y the emphasised syllables are moa difference between naughty work which we like to do and that which duty and not our own wi pa something has happened that i did not like very well but i am getting over it now you k amburgh had originally been a sailor but like many others had years ago deserted his ship gs and can go without for as long as you like nearly and never drinks anything but water h n and rows of trucks and carriages stood like black walls in the mire little hope of engin chydermatous and wary old campaigner had like ulysses seen many cities and he knew the man seeing hearing nor feeling them i do not like to be in this life as i am i have no power t d massinger of course he did it was just like his pride and disdain of a superior you may ere fleeter they were soon overtaken now like brave girls they stood to face their enemies and that she wasnt buried in the shough like the rest o them my poor tom my hearts sore f ic and revengeful one not even its owner like the root of a tree its natural character is hat a woman and especially a young woman like herself was out of place in lecturing an ass or any plumstones cherry stones and the like or he would come near to incurring the guilt lled the patriarch take my advice and be like samuel take counsel of some proved friends u w but rather enjoyed it still he did not like the idea of floating all the way down to the natives who bore them on their shoulders like children across the marshes and rivers at fi ankor ankor came down upon the platform like imperious commands as if the gods were desir lorn silicious slabs of spar flood borne like cakes of ice when spring is young burst up b ch as to say that is eternal well if you like except the law said the doctor here is a ple
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/AdaCoun.xml.txt is most about that word.
If your study carrel could be summed up in three words ("topics") then those words might be: said, said, and street. And the respective files would be: ./txt/PriMrs.xml.txt, ./txt/DomRano.xml.txt, and ./txt/PusNumb.xml.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:
Moreover, the totality of the study carrel's aboutness, can be visualized with the following pie chart:
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, time, day, mr., street, way, hand, life, eye, thing, mrs., woman, face, house, place, night, people, father, friend, word, girl, head, child, one, year) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, have, do, say, go, see, come, make, know, take, think, look, get, give, tell, find, leave, seem, hear, ask, feel, stand, turn, keep, speak) 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:
An extraction of proper nouns (Mr., Mrs., Miss, New, Maori, John, st, George, Jack, Harry, God, Zealand, Mary, Te, J., Captain, Mr, William, Tom, Maoris, M., England, Col, Frank, James) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (i, he, it, you, she, they, him, me, we, them, her, us, himself, herself, myself, themselves, itself, yourself, one, ‘, ye, ourselves, mine, yours, thee) 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.
Learning about a corpus's adjectives (good, little, old, other, great, own, young, more, many, few, such, last, first, much, long, same, poor, small, large, new, high, white, bad, full, next) and adverbs (not, so, up, then, now, out, very, only, never, as, down, here, again, away, more, too, well, there, even, still, just, ever, back, once, on) 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?"
 Once upon a time, a corpus of a million words was deemed large.
 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.
 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.
 "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.
 Concordances are one of the oldest forms of text mining, first developed in the 13th century to "read" religious documents.
 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.
Eric Lease Morgan <email@example.com>
June 11, 2019