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 42 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 32147 words long. [0] Each item in your study carrel is, on average, 765.0 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 45.0. [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: notre, dame, university, research, play, campus, visit, global, search, home, news, students, life, skip, service, student, now, school, next, faith, events, ago, contact, information, us. [3] Using the three most frequent words, the three files containing all of those words the most are ./txt/www-youtube-com-1798.txt, ./txt/www-nd-edu-3266.txt, and ./txt/twitter-com-9811.txt.

The most frequent two-word phrases (bigrams) include: notre dame, next play, play next, play now, faith service, dame views, duration minutes, ago play, notre dames, campus life, research global, global faith, admissions research, service campus, academics admissions, dame skip, dame notre, student life, contact search, content skip, real estate, mobile app, navigation skip, usa phone, years ago, and the three file that use all of the three most frequent phrases are ./txt/www-youtube-com-1798.txt ./txt/www-nd-edu-3266.txt, and ./txt/twitter-com-9811.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: event, dame, new, class, global, notre, student, university, academic, analytics, bevacqua, campus, casino, catholic, close, commencement, duration, energy, estate, faith, families, father, halls, http, investigation. 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:

challenge and the opportunity of a notre dame education has attracted bold minds from all 
ne stay connected anytime anywhere notre dame notredame twitter weve detected that javascr
ne at the university of notre dame notre dame institute for advanced study announces new d
 that investment with its students notre dame is one of fewer than institutions in the cou
ch university of notre dame search notre dame search search web people news events officed
f public affairs search notre dame notre dame in usa phone university of notre dame search
g for the lives of children global notre dame in china notre dame leaders visit hong kong 
office of the registrar grace hall notre dame in fax registrar ndedu your circle of friend
umni awarded fulbright grants news notre dame news university of notre dame skip to conten
ts closed captioning university of notre dame skip to content skip to navigation skip to s
copyright university of notre dame notre dame news notre dame in usa accessibility informa
undaries us poet laureate to visit notre dame fighting for fighting to explore the moon fi
tion minutes seconds university of notre dame views years ago play next play now rev john 
s out of her fathers shadow global notre dame in rome rev john i jenkins csc meets with ch
 dame notre dame events grace hall notre dame in usa phone calendarndedu accessibility inf
azilian judge sergio moro receives notre dame award research shelter and the storm nd engi
ad admissions office main building notre dame in nd mobile app download for apple download
world so too has the world come to notre dame approximately percent of the universitys stu
imate change accords university of notre dame skip to content university of notre dame sto
ration hours minutes university of notre dame views year ago play next play now commenceme
ation minutes second university of notre dame views months ago cc play next play now fight
nts his two older brothers went to notre dame high school run by the congregation of holy 
 calendar download event home submit an event view upcoming events architecture school of 
aw school lectures and conferences notre dame research official academic calendar ongoing 
of your academic goals super speed notre dame is building countrys largest quiet hypersoni

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 dame, and ./txt/www-nd-edu-5479.txt is most about that word.

If your study carrel could be summed up in three words ("topics") then those words might be: dame, notre, and play. And the respective files would be: ./txt/www-nd-edu-7081.txt, ./txt/twitter-com-9811.txt, and ./txt/www-youtube-com-1798.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. dame notre research - ./txt/www-nd-edu-7081.txt
  2. notre dame twitter - ./txt/twitter-com-9811.txt
  3. dame notre university - ./txt/www-nd-edu-9971.txt
  4. play dame notre - ./txt/www-youtube-com-1798.txt
  5. analytics google dame - ./txt/www-google-analytics-com-6623.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 (notre, dame, university, student, research, campus, year, search, global, news, home, play, life, service, event, skip, view, school, faith, class, information, community, twitter, contact, duration) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, have, visit, say, do, get, make, use, work, follow, give, like, play, ’, provide, help, see, find, go, fight, include, close, create, learn, take) 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 (Notre, Dame, University, Global, Search, Research, News, Campus, Skip, Home, Service, Life, School, Faith, Hall, Student, ND, Duration, Tweet, Twitter, |, Admissions, College, June, Office) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (it, we, you, he, i, they, she, us, them, me, him, her, himself, themselves, @nd_news, itself, ourselves, ‘) 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 (more, next, new, real, good, other, catholic, first, social, religious, close, academic, international, large, high, many, common, great, most, top, dame‏verified, global, right, english, several) and adverbs (now, not, ago, also, up, more, just, so, well, better, back, really, most, only, as, very, again, in, out, here, second, then, always, even, about) 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.

Eric Lease Morgan <emorgan@nd.edu>
June 11, 2019