Basic Reports

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 33 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 318,291 words long. [0] Each item in your study carrel is, on average, 9,645 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 illustrate the overall size of your study carrel.

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histogram of sizes
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box plot of sizes


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 51. [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 illustrate the overall readability of your study carrel.

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histogram of readability
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box plot of readability

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: [3]

one, fiction, data, also, words, texts, novels, corpus, literary, see, reading, new, gender, century, social, genre, figure, novel, text, may, different, work, cultural, analysis, use, two, language, research, time, history, historical, us, many, first, word, books, information, digital, poets, like, works, might, women, used, number, university, model, using, even, set

Using the three most frequent words, the three files containing all of those words the most are ./txt/culturalanalytics-org-9798.txt, ./txt/culturalanalytics-org-9021.txt, and ./txt/culturalanalytics-org-8871.txt.

The most frequent two-word phrases (bigrams) include:

new york, cultural analytics, science fiction, university press, nineteenth century, digital humanities, detective fiction, female poets, et al, topic modeling, poet heterosexual, machine learning, twentieth century, performance styles, literary history, tang kristensen, dimensionality reduction, pitch range, critical search, ted underwood, sampled recordings, data sets, th century, poet voice, andrew piper, bloom filter, poetry reading, national biography, pitch speed, official histories, male poets, speaking rate, author gender, intratextual novelty, early modern, foreign corpus, principal component, analytics issn, articles data, contact us, sets clusters, ca contact, article doi, clusters debates, search articles, one another, rhythmic complexity, allows us, late nineteenth, social networks

And the three file that use all of the three most frequent phrases are ./txt/culturalanalytics-org-9798.txt ./txt/culturalanalytics-org-9021.txt, and ./txt/culturalanalytics-org-8871.txt.

While often deemed superficial or sophomoric, rudimentary frequencies and their associated "word clouds" can be quite insightful:

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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:

literary, data, word, text, novels, figures, genres, likely, new, words, differ, digitized, feature, fictional, languages, news, novel, press, researcher, americans, books, category, centuries, century, character, characters, classifications, critically, criticism, cultures, differently, differs, females, fictions, figure, gendering, geographically, gothic, historicity, human, includes, like, modeled, modern, modernity, poetry, poets, read, social, texts

And now word clouds really begin to shine:

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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 fiction, and Social Characters: The Hierarchy of Gender in Contemporary English-Language Fiction « CA: Journal of Cultural Analytics is most about that word.

If your study carrel could be summed up in three words ("topics") then those words and their significantly associated titles include:

  1. data - Measuring Modernist Novelty « CA: Journal of Cultural Analytics
  2. reading - Beyond Poet Voice: Sampling the (Non-) Performance Styles of 100 American Poets « CA: Journal of Cultural Analytics
  3. fiction - Race, Writing, and Computation: Racial Difference and the US Novel, 1880-2000 « CA: Journal of Cultural Analytics

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. fiction, corpus, literary - Nation, Ethnicity, and the Geography of British Fiction, 1880-1940 « CA: Journal of Cultural Analytics
  2. fiction, genre, novel - Race, Writing, and Computation: Racial Difference and the US Novel, 1880-2000 « CA: Journal of Cultural Analytics
  3. data, gender, women - Measuring Modernist Novelty « CA: Journal of Cultural Analytics
  4. poets, poet, poetry - Beyond Poet Voice: Sampling the (Non-) Performance Styles of 100 American Poets « CA: Journal of Cultural Analytics
  5. reading, topic, books - Critical Search: A Procedure for Guided Reading in Large-Scale Textual Corpora « CA: Journal of Cultural Analytics

Moreover, the totality of the study carrel's aboutness, can be visualized with the following pie chart:

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topic model

Noun & 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 helps you answer what questions; "What is discussed in this collection?":

fiction, data, words, texts, novels, corpus, %, century, gender, text, genre, work, novel, analysis, history, time, figure, reading, poets, number, books, women, research, authors, information, language, way, works, terms, word, features, example, model, results, characters, topic, case, categories, space, study, writers, scholars, science, genres, level, methods, set, poetry, literature, character

An enumeration of the verbs 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:

is, are, be, have, was, were, see, has, do, used, been, using, does, use, based, associated, find, given, make, had, found, read, including, reading, did, made, being, include, published, ''s, understand, appear, written, identify, suggests, identified, known, suggest, makes, included, set, tend, according, seem, seen, say, shows, compared, making, related

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Proper Nouns

An extraction of proper nouns helps you determine the names of people and places in your study carrel.

↩, university, london, press, new, journal, york, poet, cultural, literary, english, james, john, fiction, digital, analytics, figure, gothic, oxford, alger, david, data, american, ca, cambridge, odnb, al, et, social, bible, doi, smith, novel, humanities, language, matthew, science, britain, hathi, national, stanford, research, underwood, scotland, up, heterosexual, thomas, gender, andrew, literature

An analysis of personal pronouns 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?"

we, it, our, their, they, its, i, us, his, them, he, her, she, you, itself, one, my, themselves, me, him, your, herself, himself, y, ourselves, ye, myself, ours, ''s, oneself, theirs, thy, yours, yourself, ''em, 0,0718, 0,8511, all).23, difference.2, hers,, mine, representation,13even, u, warns,"they, ya, yuh, 至

Below are words cloud of your study carrel's proper & personal pronouns.

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proper nouns
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Adjectives & Verbs

Learning about a corpus's adjectives and adverbs 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?"

other, such, different, literary, more, social, many, historical, same, cultural, new, female, particular, large, first, digital, critical, american, important, nineteenth, similar, male, most, individual, possible, early, high, single, textual, significant, specific, own, linguistic, computational, second, black, white, much, top, common, human, contemporary, average, likely, racial, few, certain, larger, full, least

not, more, also, only, most, as, even, well, so, here, out, however, often, then, very, less, n''t, rather, up, just, thus, perhaps, together, indeed, especially, still, too, far, much, relatively, first, highly, instead, frequently, simply, significantly, again, almost, now, always, all, quite, yet, generally, already, at, closely, finally, back, is

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