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 88 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 4354628 words long.  Each item in your study carrel is, on average, 49484.0 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 89.0.  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, will, one, matt, now, little, like, know, man, eyes, well, face, see, go, back, time, come, old, came, two, away, never, must, mrs, dont.  Using the three most frequent words, the three files containing all of those words the most are ./txt/53416.txt, ./txt/50440.txt, and ./txt/44828.txt.
The most frequent two-word phrases (bigrams) include: motor matt, new york, motor matts, said matt, dont know, page changed, old man, young man, dark eyes, will go, let us, come back, last night, young girl, dont think, answered matt, frank merriwells, charles garvice, ive got, go back, tsan ti, get away, blue eyes, air ship, dont want, and the three file that use all of the three most frequent phrases are ./txt/53533.txt ./txt/53466.txt, and ./txt/52891.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, said, likely, looking, look, looked, looks, little, chapter, liked, old, away, good, came, man, eye, eyes, boy, likes, boys, face, faces, hand, hands, matt. 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:
you have made meall of usso happy ay he said and he smiled at her i am very glad heaven k business a ladyat this time of the night said la reine blanche lifting her arched brows ve has no terror to change or alter a love like minee w w precious hastened to the nearest m the sons of the rising sun the mail boat said the news that the steamer had escaped the sa e at first but it was not likely to last said the maid pursuing her argument with the free breath it is not at all necessary sir he said recovering from his halfdazed stupidity the oing to have this refitted for you leith said as he bent above her dark head caressingly i ster stood upright watching him go on he said in a hard metallic voice i brought her here of yours you were not mistaken when you said that you thought you recognized his voice th n front of him youd better sit down murg said newt the two men stared at each other the br eath and just slip out before they close said he but you dont know the whole of it not by lancaster it would be a mésalliance she said with her eyes full on his face as she quoted ing from the house across the wilderness like a crowd of scared savages when the carriage here he had stopped a figure that looked like a creeping indian he watched it till it vani d leveling it at the head of the officer said give me your sword the man meekly obeyed wit an breathed hard her bosom rose and fell like the waves of the sea and she could hardly ke ke ganders when pursuing somebody master said it was terrible to witness the mute agony of t know you were in the house my lord she said i have just entered it he replied do not let l bird jumps into the air and a big bird like a condor has to take a run before its wings ertha you raise my hopeswhy do you speak like that you know somethingdoes she care for me silence for a moment then austin ambrose said with a meditative smile and are you going to s brown said you were a real ladypersons like her make such mistakesbut i see that she was pened to meet like her any voice i heard like hers this one for instance isis the perspira e agreed between us that nothing need be said i was ashamed of having gone to the old fort at could you do i dont know but i should like to try to find the thief forthis gentleman i
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/47179.txt is most about that word.
If your study carrel could be summed up in three words ("topics") then those words might be: said, matt, and said. And the respective files would be: ./txt/53416.txt, ./txt/47491.txt, and ./txt/44658.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 (matt, man, eye, hand, time, face, girl, day, way, boy, mrs., heart, room, life, word, mr., night, moment, chapter, woman, one, head, thing, lady, voice) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, have, do, say, go, come, see, know, make, get, look, take, think, tell, give, find, ask, leave, turn, hear, answer, seem, let, stand, want) 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 (Matt, Mrs., Mr., Carl, Dick, Miss, Ned, Lady, Jack, Motor, Captain, McGlory, Frank, New, Leslie, Lord, Herc, York, God, Margaret, Yorke, St., Blair, Stella, Kathleen) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (i, he, you, it, she, him, me, her, they, we, them, us, himself, herself, myself, yourself, themselves, one, itself, ye, mine, yours, hers, ''em, ''s) 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, young, other, more, great, own, last, white, poor, few, long, first, beautiful, much, dark, same, dead, many, dear, right, such, sure, strange) and adverbs (not, so, up, then, out, now, here, down, back, very, away, never, as, only, just, too, again, there, well, off, on, more, all, ever, even) 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 <firstname.lastname@example.org>
June 11, 2019