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 28 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 90,559 words long. [0] Each item in your study carrel is, on average, 3,483 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 52. [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]

data, ai, science, human, will, intelligence, new, model, may, learning, research, one, machine, systems, models, also, use, drug, songs, two, analysis, many, time, information, set, work, features, artificial, https, indication, testing, statistical, engineering, prediction, different, see, song, used, jordan, need, mccartney, table, figure, us, principles, based, al, computing, missing, using

Using the three most frequent words, the three files containing all of those words the most are ./txt/hdsr-mitpress-mit-edu-9193.txt, ./txt/hdsr-mitpress-mit-edu-8904.txt, and ./txt/hdsr-mitpress-mit-edu-8777.txt.

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

data science, machine learning, artificial intelligence, et al, harvard data, science review, globe envelope, missing data, indication pairs, testing sets, imitative ai, computer science, testing set, deep learning, data scientists, logistic regression, human intelligence, happened yet, rss terms, international license, help feature, explore pricing, envelope manualissue, pricing help, labels sort, creative commons, commons attribution, community newsletterarrow, branch public, review rss, public caret, published jun, manualissue caret, terms globe, archived labels, scope globe, feature community, review searchlogin, signup home, indication groups, mccartney songs, training set, engineering discipline, diatonic scale, revolution hasn, ai systems, musical features, life cycle, highlight text, supplementary materials

And the three file that use all of the three most frequent phrases are ./txt/hdsr-mitpress-mit-edu-9193.txt ./txt/hdsr-mitpress-mit-edu-8904.txt, and ./txt/hdsr-mitpress-mit-edu-8777.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:

data, humans, sciences, jordan, feature, http, humanities, artificially, currently, difference, drugs, human, humanity, intelligent, modeled, new, news, research, researcher, researchers, statistics, table, algorithm, arxiv, authorships, beatles, behaviors, businesses, chord, cloud, cluster, codalab, communications, computation, cycle, datasets, dec, decisions, digitize, doi, educated, engine, engineered, ethically, experience, figures, financial, floridi, greek, https

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 data, and 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 - Machine Learning with Statistical Imputation for Predicting Drug Approvals · Harvard Data Science Review
  2. data - A Balanced Perspective on Prediction and Inference for Data Science in Industry · Harvard Data Science Review
  3. songs -

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. data, ai, human -
  2. data, ai, science - A Balanced Perspective on Prediction and Inference for Data Science in Industry · Harvard Data Science Review
  3. data, science, testing - Ambitious Data Science Can Be Painless · Harvard Data Science Review
  4. ai, data, principles - A Unified Framework of Five Principles for AI in Society · Harvard Data Science Review
  5. dialogue, grosz, evident - Some Reflections on Michael Jordan’s Article “Artificial Intelligence—The Revolution Hasn’t Happened Yet” · Harvard Data Science Review

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?":

data, science, model, machine, models, research, systems, intelligence, drug, songs, ai, time, features, analysis, indication, information, learning, testing, prediction, song, work, engineering, example, inference, field, scale, development, principles, training, results, sets, statistics, envelope, trial, methods, researchers, set, terms, authorship, approach, world, humans, note, pairs, people, challenges, number, %, dataset, datasets

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, has, was, do, were, see, used, based, using, been, missing, make, use, need, create, driven, set, learning, know, had, including, does, making, being, given, known, retrieved, written, provide, consider, find, think, agree, developed, published, made, help, include, makes, happened, take, understand, apply, develop, build, called, having

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Proper Nouns

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

ai, data, science, jordan, mccartney, j., al, m., et, lennon, intelligence, harvard, d., artificial, review, s., a., l., r., p2app, table, c., b., press, mar, journal, e., research, statistical, michael, university, ds, google, new, p., andi, international, ml, k., codalab, g., auc, national, community, figure, floridi, help, ii, p3app, t.

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, i, their, they, its, them, us, my, you, he, his, me, ‘, one, your, itself, themselves, she, her, cj, ourselves, y, myself, ’s, him, theirs, y(i)\boldsymbol{y}_{(i)}y(i)​, yij, himself,,,, oneself, ours, y=(y1,, –0.096

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?"

human, such, new, other, many, more, different, same, statistical, predictive, diatonic, current, artificial, large, important, scientific, computational, social, musical, first, specific, major, good, most, high, intelligent, melodic, particular, complete, general, modern, own, standard, complex, deep, ethical, useful, public, better, real, individual, able, multiple, possible, several, much, available, best, long, second

not, more, also, only, up, well, even, down, e.g., as, most, out, however, now, often, so, very, just, n’t, then, yet, thus, still, indeed, rather, first, here, already, above, much, •, far, less, together, therefore, currently, generally, perhaps, instead, on, particularly, too, back, finally, especially, better, simply, typically, fully, is

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