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 1190 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 25107861 words long. [0] Each item in your study carrel is, on average, 21099.0 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 46.0. [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: de, shall, article, party, free, excl, agreement, la, may, parties, materials, en, heading, chapter, used, products, services, product, goods, trade, value, including, within, manufacture, measures. [3] Using the three most frequent words, the three files containing all of those words the most are ./txt/dh-crc-nd-edu-3722.txt, ./txt/dh-crc-nd-edu-4361.txt, and ./txt/dh-crc-nd-edu-5019.txt.

The most frequent two-word phrases (bigrams) include: excl excl, free free, party shall, materials used, de la, exworks price, parties shall, member states, party may, official journal, european union, none none, joint committee, staging category, description base, textile materials, en el, un cambio, unbound except, de cualquier, bhm brb, kind used, base rate, classified within, customs duties, and the three file that use all of the three most frequent phrases are ./txt/dh-crc-nd-edu-834.txt ./txt/dh-crc-nd-edu-9756.txt, and ./txt/dh-crc-nd-edu-9024.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: shall, articles, agreements, party, good, included, chapters, page, trading, production, article, measure, est, service, trade, country, traded, yarns, include, product, total, rates, similar, includes, meaning. 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:

cedures in promoting trade facilitation shall make cooperative efforts to a make use of in
ts listed in the annex to this appendix shall be subject to the provisions of this protoco
agreement any remaining duty and charge shall be abolished ceegen customs duties and charg
 matters within the common jurisdiction shall be regulated by the republic article represe
 the provinces other than new brunswick shall not be subject to such dues section no prope
uting investor referred to in paragraph shall deliver a copy of its request to the disputi
ll inform the other party forthwith and shall submit to it as soon as possible a timetable
ing parties to this end the secretariat shall submit to the council proposals concerning t
ioning of this section in particular it shall have the following responsibilities and func
ed on the commissions review each party shall encourage its respective competent authoriti
 shall be submitted to the director who shall transmit it to the parties if three or more 
of a dispute under article each country shall allow transfers referred to in paragraph of 
 railway connections the partner states shall in particular a adopt common policies for th
he arbitration of three arbitrators who shall be nationals of either india or bhutan chose
dification or withdrawal of concessions shall be treated as an amendment to annex and shal
article entry into force this agreement shall enter into force on the thirtieth day after 
rticle right to act none of the parties shall have the right to act in its legislation aga
matters the objectives of the committee shall be to enhance each partys implementation of 
state of lithuania and their boundaries shall be determined by law article citizenship of 
eptance of this obligation both parties shall not permit the importation manufacture and s
s carbon electrodes and other carbon articles of a kind used for electrical purposes insul
tive advantages the economic activities shall not jeopardize the ecosystem nor should they
sive abolition of existing restrictions shall be effected in accordance with the provision
uard measure on the good such a measure shall terminate at the end of the transition perio
ry of the united states free apparel articles both cut or knittoshape and sewn or otherwis

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 shall, and ./txt/dh-crc-nd-edu-477.txt is most about that word.

If your study carrel could be summed up in three words ("topics") then those words might be: 10, shall, and excl. And the respective files would be: ./txt/dh-crc-nd-edu-9196.txt, ./txt/dh-crc-nd-edu-5019.txt, and ./txt/dh-crc-nd-edu-4284.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. shall article party - ./txt/dh-crc-nd-edu-4317.txt
  2. 10 free 01 - ./txt/dh-crc-nd-edu-1221.txt
  3. excl materials 10 - ./txt/dh-crc-nd-edu-4284.txt
  4. la en el - ./txt/dh-crc-nd-edu-5019.txt
  5. demás 10 15 - ./txt/dh-crc-nd-edu-8835.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 (%, article, party, excl, product, agreement, material, free, de, chapter, service, parties, good, measure, trade, value, manufacture, law, annex, part, change, country, paragraph, tariff, provision) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, have, include, use, provide, exceed, do, make, head, apply, originate, take, contain, refer, establish, relate, require, follow, mean, subhead, set, agree, concern, consider, work) 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 (Party, Excl, Free, Agreement, Parties, DE, Article, Annex, States, el, EN, Committee, Community, Republic, State, Manufacture, E-8, European, Member, ET, OU, E-0, Trade, Council, Los) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (y, it, i, they, he, them, him, us, themselves, itself, me, ‘, we, em, ya, you, she, himself, lo, o, cu, her, bl, wh, ng) 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, such, free, more, la, less, similar, public, para, natural, necessary, regional, subject, economic, financial, international, appropriate, good, textile, relevant, national, technical, particular, otra, applicable) and adverbs (not, out, otherwise, thereof, only, up, as, also, free, down, however, well, so, directly, further, wholly, least, ever, at, no, elsewhere, mutually, solely, more, less) 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.