2b:null 2c:["$","title",null,{"children":"A5 What’s Shakespeare About Anyways? | span728 | Observable"}] 31:Tff7,md` ## Introduction

Rosalind and Celia in the forest. Photo: [National Museums Liverpool](https://www.liverpoolmuseums.org.uk/artifact/rosalind-and-celia)

I was sitting at the kitchen table reading Shakespeare’s “As You Like It” for a class when I noticed something intriguing. In my edition of the play, the editors had included a rather cheeky footnote: “**wit**: cleverness, intelligence. The word occurs 24 times in the play, with a wide range of functions and meanings” (Oxford AYLI, pg. 107).
These editors even suggested that frequently used words could tell us the essence of a story; that footnote went on to say, “this amount of usage ranks As You Like It third after Love’s Labor Lost (32) and Much Ado About Nothing (29), two other [Shakespeare] comedies with notably witty heroines” (Oxford AYLI, pg. 107). So the common occurrence of wit somehow proved that these three plays were witty. Word frequency could be interesting, I thought.

And that is why I decided to learn some text analysis techniques and apply them to “As You Like It”. Come see what I learned. **** ## Some Context on the Play “As You Like It” is one of Shakespeare’s comedy plays (comedy because no one dies), first published in 1623. The story goes something like this: when Rosalind is unjustly banished from her uncle’s court, she and her cousin Celia escape into the forest of Arden to start a new life. In Arden, they philosophize, tell lewd jokes, and encounter quirky characters, including their eventual husbands. The play ends in marriage for both girls. [Here is a link to the full text of the play.](http://shakespeare.mit.edu/asyoulikeit/full.html)
“As You Like It” is notable for many reasons; for centuries, scholars have been fascinated by the way it explores the pastoral (the idyllic world of shepherds, music, and love first introduced to the Western world by Virgil), the intrinsic rivalry between brothers, and female-female affection (simultaneously erotic and protective and familial). Fun fact: the play is also the origin of the famous quote “All the world’s a stage”.
A lot to unpack right? Let's dive in. To start, I pulled the text from the link above and split it into five documents (one for each act). [Click here to see those docs](https://github.com/span728/asyoulikeit/tree/master/words), and feel free to use them for your own analyses! **** ## Starting with the Basics: Word Frequency With text analysis, word frequency is the easiest (and therefore best) place to start. To do this, I decided to learn about TF-IDF, which stands for “term frequency - inverse document frequency”. According to [this article](https://observablehq.com/@kerryrodden/introduction-to-text-analysis-with-tf-idf) by Kerry Rodden, “ TF-IDF is a method for term weighting, where each of the documents in a collection is characterized using weights assigned to the words (or terms) present in it. This makes it possible to rank the documents according to their estimated relevance to a search query. These concepts are still relevant to modern search engines and other kinds of text analysis”. In other words, TF-IDF helps us understand a document in the context of a related set of texts. For this project, I chose to treat each of the play's five acts as a separate document.
Raw word frequency is not that useful, because so many words in the English language are generic (“the”, “and”, “it”, etc.), and don’t tell us that much about a text’s thematic material. Fortunately, TF-IDF includes a preset array of stopwords, aka all the generic words that appear in most documents and can therefore be excluded. I also wrote in a custom stopwords array to filter out Shakespearian stopwords that TF-IDF didn’t catch (see Supporting Code). Using these, I moved forward to find the most frequent terms in each of the play's five acts. `32:T4b7,md` **** Kind of fun, right? My intial thoughts were:

The characters' names occur EXTREMELY frequently: Celia, Rosalind, Duke, Orlando, Jaques, etc.
Other thematic groupings have also appeared: “love”, “like”, and “heart” seem to go together as terms of love, while “father”, “brother”, and “master" are terms of relation.

That’s cool! But we can take this further.
**** ## The actual TF-IDF: Any Findings?
If we adjust the weight each term receives for a particular document by taking into account how many other documents in the collection contain that term, we can get the collection frequency, or inverse document frequency (Perkins). Frequent terms get the lowest weights, and infrequent terms get the highest. Once we adjust for document length (which is necessary because a longer document would be weighted higher purely because it has more raw occurrences of terms), we can find the most important weighted term per act. In other words, we can find the word that uniquely "identifies" each act.
This table shows the most frequent (non-stopword) word and the top TF-IDF weighted word per act. `33:T70d,md` Admittedly, not that enlightening, but kind of comedic. “William” makes sense as the top TF-IDF word of Act V because the comic relief character William enters here! Oh well.
But “verses” for Act III is kind of interesting. Act III is the “courtship” act; the hero, Orlando, has fallen in love with Rosalind, and he knows no other way to express his affection than to run around the forest and tack (bad) love poetry onto the trees; the opening of Act III Scene II reads, “Hang there, my verse, in witness of my love… O Rosalind, these trees shall be my books” (Oxford AYLI, 3.2 Lines 1 & 4). Apparently the algorithm has picked up on this too; Orlando’s romantic verses are the star of the show. **** ## LDA to the Rescue: Algorithmically Identifying Themes After finishing the exercise above, I realized that I wanted to know if there was a systematic way to identify themes in the play. I turned to LDA (Latent Dirichlet Allocation), a topic modeling method. According to [this article](https://towardsdatascience.com/latent-dirichlet-allocation-lda-9d1cd064ffa2) from Towards Data Science, topic modeling is “a method for unsupervised classification of documents… which finds some natural groups of items (topics) even when we’re not sure what we’re looking for”. In other words, LDA aims to identify topics or themes of a text by looking at the words it is made up of, and can do so without human guidance!
Using [this example](https://towardsdatascience.com/topic-modeling-and-latent-dirichlet-allocation-in-python-9bf156893c24), I ran As You Like It through LDA to identify 10 topics, made up of 10 words each. Here are the results visualized, where each word's block length is proportional to the weight it contributes to the topic.
`34:T915,md` **If you’re interested in the underlying code, click here to see the ["As You Like It" CSV file](https://github.com/span728/asyoulikeit/blob/master/ayli_text.csv), [my Python script](https://github.com/span728/asyoulikeit/blob/master/nlp_python.py), and [the results it produced](https://github.com/span728/asyoulikeit/blob/master/results.txt).

I invite you to take an exploratory scroll through the viz before reading my interpretations!

My take: I looked through the word sets and assigned titles to each one. Although some of the groupings were unexpected (like “hear”, “poor”, and “food” in Communication), LDA did latch onto many of the same terms and broad themes as I did. Based on the two most popular words ("Rosalind" and "love"), LDA and I agree that the play revolves around Rosalind and the people she loves (Orlando, her father, and Celia, among others).
But it’s a far more complex story than that: the play is also interested in the idealism of the forest (Love in the Forest), how we fall in love (Unrequited Love), and how we express those feelings (Emotions, Communication). Not to mention the messy familial relationships that span the play, between brothers, parents and children, and cousins (Brothers, Young Women, Banishment). Simply, wow. **** ## Concluding Thoughts LDA was able to give us a grab bag of (slightly less than) 100 words that together, paint a pretty comprehensive picture of “As You Like It”. They reveal a text that is at once a romance, a comedy, and a piece of philosophy, with interest in all of the themes mentioned above. Certainly, a lot of interpretive work went into this project, but that is the driving force behind the formal study of literature! I hope my analysis has taught you a bit more about why this play is special, and how to approach it critically, but more importantly I hope you enjoyed yourself! Thank you for joining this chapter.

Rosalind, Celia, and Touchstone the clown. Photo: [Met Museum](https://www.metmuseum.org/art/collection/search/774316)

**** ## Contact Me If you have questions about my process or just want to chat Shakespeare, email me at sp3507@columbia.edu! **** `35:[{"id":"37a2184ea47e4498b181d0d135523e25baacbdfa5ca8ce391474b559ea2d2eae7d6d01173a0f1fd979766a0a1fee1540ccf6a80533514b98080aeda5ce257551","href":"https://static.observableusercontent.com/files/37a2184ea47e4498b181d0d135523e25baacbdfa5ca8ce391474b559ea2d2eae7d6d01173a0f1fd979766a0a1fee1540ccf6a80533514b98080aeda5ce257551","name":"roscel@1.jpeg","type":"image/jpeg","lastModified":1588444343330.917,"size":10378},{"id":"b64b2cf9b732407ff1a6449ea69190f2914458313db518f518d1e5d2844f146d17af18f16c85571faa0fa91e572fecbcef08f36efcdb91721bf76c8090b88943","href":"https://static.observableusercontent.com/files/b64b2cf9b732407ff1a6449ea69190f2914458313db518f518d1e5d2844f146d17af18f16c85571faa0fa91e572fecbcef08f36efcdb91721bf76c8090b88943","name":"keywords.csv","type":"text/csv","lastModified":1588199501861.456,"size":2754},{"id":"804ff62aeef57cf473237b245a2b19bbf58740853503979326fd99e60d61185011f017ecdb47afc8fe67453cc4ae79a6b09be24cb66102764af7a3a7eaf6eacb","href":"https://static.observableusercontent.com/files/804ff62aeef57cf473237b245a2b19bbf58740853503979326fd99e60d61185011f017ecdb47afc8fe67453cc4ae79a6b09be24cb66102764af7a3a7eaf6eacb","name":"touchstone+rc.jpeg","type":"image/jpeg","lastModified":1588468147178.005,"size":372995}] 2f:{"id":"a8be8c4fe0d6f4a9","slug":"a5-w-tfidf","title":"A5 What’s Shakespeare About Anyways?","thumbnail":"87313591ae7a67a8571df8aff1f8cdf0c459a1e1cb4df6cacccace466b100430","access_level":"public","create_time":"$D2020-04-13T01:00:18.891Z","update_time":"$D2020-05-06T00:53:33.835Z","view_time":null,"first_public_time":"$D2020-04-25T20:08:30.548Z","fork":false,"stars":4,"starred":false,"owner":{"type":"team","id":"7ced62cecadd0b6a","login":"span728","name":"","avatar_url":"https://avatars-next.observableusercontent.com/ff8535c5ac0e5a305dc4e28583edf6e02b9dd0e4330e748b3a5abf3ab73b8e9a","tier":"starter_2024"},"version":431,"publish_version":431,"role":null,"authored":false,"authors":[{"type":"individual","id":"425e641f78ad13da","login":"span728","name":"","avatar_url":"https://avatars-next.observableusercontent.com/ff8535c5ac0e5a305dc4e28583edf6e02b9dd0e4330e748b3a5abf3ab73b8e9a","tier":null}],"members":[{"type":"individual","id":"425e641f78ad13da","login":"span728","name":"","avatar_url":"https://avatars-next.observableusercontent.com/ff8535c5ac0e5a305dc4e28583edf6e02b9dd0e4330e748b3a5abf3ab73b8e9a","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":2,"collection":null,"type":"notebook","body":{"title":"A5 What’s Shakespeare About Anyways?","cells":[{"id":0,"value":"md`# A5 What’s Shakespeare About Anyways?`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":221,"value":"md`Learning more about “As You Like It” through introductory text analysis\n
`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":246,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":62,"value":"md` **ACT I**`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":73,"value":"{\n const document = corpus.getDocument(\"one\");\n const tfTableMinusStopwords = table(\n document\n .getUniqueTerms()\n .filter(d => !corpus.getStopwords().includes(d))\n .map(t => ({\n Term: t,\n Frequency: document.getTermFrequency(t)\n }))\n .sort((a, b) => d3.descending(a.Frequency, b.Frequency))\n .slice(0, 15)\n );\n tfTableMinusStopwords.style.maxWidth = \"200px\";\n return tfTableMinusStopwords;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":89,"value":"md` **ACT II**`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":100,"value":"{\n const document = corpus.getDocument(\"two\");\n const tfTableMinusStopwords = table(\n document\n .getUniqueTerms()\n .filter(d => !corpus.getStopwords().includes(d))\n .map(t => ({\n Term: t,\n Frequency: document.getTermFrequency(t)\n }))\n .sort((a, b) => d3.descending(a.Frequency, b.Frequency))\n .slice(0, 15)\n );\n tfTableMinusStopwords.style.maxWidth = \"200px\";\n return tfTableMinusStopwords;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":90,"value":"md` **ACT III**`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":105,"value":"{\n const document = corpus.getDocument(\"three\");\n const tfTableMinusStopwords = table(\n document\n .getUniqueTerms()\n .filter(d => !corpus.getStopwords().includes(d))\n .map(t => ({\n Term: t,\n Frequency: document.getTermFrequency(t)\n }))\n .sort((a, b) => d3.descending(a.Frequency, b.Frequency))\n .slice(0, 15)\n );\n tfTableMinusStopwords.style.maxWidth = \"200px\";\n return tfTableMinusStopwords;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":92,"value":"md` **ACT IV**`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":112,"value":"{\n const document = corpus.getDocument(\"four\");\n const tfTableMinusStopwords = table(\n document\n .getUniqueTerms()\n .filter(d => !corpus.getStopwords().includes(d))\n .map(t => ({\n Term: t,\n Frequency: document.getTermFrequency(t)\n }))\n .sort((a, b) => d3.descending(a.Frequency, b.Frequency))\n .slice(0, 15)\n );\n tfTableMinusStopwords.style.maxWidth = \"200px\";\n return tfTableMinusStopwords;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":94,"value":"md` **ACT V**`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":120,"value":"{\n const document = corpus.getDocument(\"five\");\n const tfTableMinusStopwords = table(\n document\n .getUniqueTerms()\n .filter(d => !corpus.getStopwords().includes(d))\n .map(t => ({\n Term: t,\n Frequency: document.getTermFrequency(t)\n }))\n .sort((a, b) => d3.descending(a.Frequency, b.Frequency))\n .slice(0, 15)\n );\n tfTableMinusStopwords.style.maxWidth = \"200px\";\n return tfTableMinusStopwords;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":278,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":128,"value":"{\n const statesTable = table(\n corpus.getDocumentIdentifiers().map(d => ({\n Act: html`${d}`,\n \"Most frequent (non-stopword) term\": corpus\n .getDocument(d)\n .getUniqueTerms()\n .filter(t => !corpus.getStopwords().includes(t))\n .map(t => ({\n Term: t,\n Frequency: corpus.getDocument(d).getTermFrequency(t)\n }))\n .sort((a, b) => d3.descending(a.Frequency, b.Frequency))[0].Term,\n \"Top weighted term via TF-IDF\": corpus.getTopTermsForDocument(d)[0][0]\n }))\n );\n return statesTable;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":289,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":199,"value":"chart = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height])\n .style(\"overflow\", \"visible\");\n\n const bar = svg.append(\"g\")\n .selectAll(\"g\")\n .data(series)\n .enter().append(\"g\")\n .style(\"stroke\", \"black\")\n .attr(\"stroke-width\", 2)\n .attr(\"fill\", \"white\")\n .selectAll(\"rect\")\n .data(d => d)\n .join(\"rect\")\n .attr(\"x\", d => x(d[0]))\n .attr(\"y\", (d, i) => y(d.data.name))\n .attr(\"width\", d => x(d[1]) - x(d[0]))\n .attr(\"height\", y.bandwidth());\n\n svg.append(\"g\")\n .call(xAxis);\n\n svg.append(\"g\")\n .call(yAxis);\n \n svg.call(chartTitle);\n \n svg.append(\"g\").attr(\"id\", \"annotation\");\n\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":296,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":418,"value":"md`\n## Sources\nhttps://observablehq.com/@kerryrodden/introduction-to-text-analysis-with-tf-idf\nhttps://observablehq.com/@jameshahn2/introduction-to-text-analysis-with-tf-idf\nhttps://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/\nhttp://blog.echen.me/2011/06/27/topic-modeling-the-sarah-palin-emails/\nhttps://towardsdatascience.com/latent-dirichlet-allocation-lda-9d1cd064ffa2\nhttps://towardsdatascience.com/topic-modeling-and-latent-dirichlet-allocation-in-python-9bf156893c24\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":347,"value":"md`\n## Supporting Code\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":370,"value":"md`\n### For LDA:\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":202,"value":"hover1 = {\n const svg = d3.select(chart);\n \n // used to test out interactivity in this cell\n const status = html`hover: none`;\n\n rects.on(\"mouseover.hover1\", function(d) {\n let me = d3.select(this);\n \n me.attr(\"fill\", \"#BAFFE0\");\n \n annotations.insert(\"text\")\n .attr(\"id\", \"label\")\n .attr(\"x\", me.attr(\"x\"))\n .attr(\"y\", me.attr(\"y\"))\n .attr(\"dx\", me.attr(\"width\")/2)\n .attr(\"dy\", (me.attr(\"height\")/2)+4)\n .attr(\"text-anchor\", \"middle\")\n .attr(\"font-family\", \"sans-serif\")\n .text(d.key);\n\n // show what we interacted with\n d3.select(status).text(\"hover: \" + d.key);\n });\n\n rects.on(\"mouseout.hover1\", function(d) {\n let me = d3.select(this);\n \n me.attr(\"fill\", \"white\");\n \n annotations.select(\"text#label\").remove();\n d3.select(status).text(\"hover: none\");\n });\n \n return status;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":187,"value":"data1 = d3.csvParse(await FileAttachment(\"keywords.csv\").text(), (d, i, columns) => (d3.autoType(d), d.total = d3.sum(columns, c => d[c]), d)).sort((a, b) => b[\"rosalind\"] / b.total - a[\"rosalind\"] / a.total)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":193,"value":"series = d3.stack()\n .keys(data1.columns.slice(1))\n .offset(d3.stackOffsetExpand)\n (data1)\n .map(d => (d.forEach(v => v.key = d.key), d))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":195,"value":"rects = d3.select(chart).selectAll(\"rect\");","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":197,"value":"annotations = d3.select(chart).select(\"g#annotation\");","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":183,"value":"x = d3.scaleLinear()\n .range([margin.left, width - margin.right])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":185,"value":"y = d3.scaleBand()\n .domain(data1.map(d => d.name))\n .range([margin.top, height - margin.bottom])\n // .padding(0.1)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":208,"value":"chartTitle = g => g.append(\"text\")\n .attr(\"font-family\", \"sans-serif\")\n .attr(\"font-size\", 30)\n .attr(\"x\", width/2)\n .attr(\"y\", 50)\n .attr(\"text-anchor\", \"middle\")\n .text(\"The Breakdown: Top Ten Topics According to LDA\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":181,"value":"xAxis = g => g\n .attr(\"transform\", `translate(0,${margin.top})`)\n // .call(d3.axisTop(x).ticks(width / 100, \"%\"))\n .call(g => g.selectAll(\".domain\").remove())","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":179,"value":"yAxis = g => g\n .attr(\"transform\", `translate(${margin.left},0)`)\n .call(d3.axisLeft(y).tickSizeOuter(0))\n .call(g => g.selectAll(\".domain\").remove())","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":177,"value":"height = data1.length * 55 + margin.top + margin.bottom","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":175,"value":"margin = ({top: 60, right: 10, bottom: 0, left: 80})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":367,"value":"md`\n### For TF-IDF:\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":38,"value":"useStopwords = true","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":40,"value":"customStopwords = [\"shall\", \"thee\", \"thou\", \"le\", \"thy\", \"well\", \"go\", \"yet\", \"come\", \"enter\", \"hath\", \"first\", \"good\", \"man\", \"one\", \"say\", \"take\", \"see\", \"make\", \"might\", \"tis\", \"sir\", \"let\", \"us\", \"upon\", \"every\", \"may\", \"must\", \"de\", \"quoth\", \"sans\"]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":28,"value":"metadata = d3.csv(\n \"https://raw.githubusercontent.com/span728/asyoulikeit/master/index.csv\"\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"acts = metadata.map(d => d.act);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":23,"value":"files = metadata.map(d => `https://raw.githubusercontent.com/span728/asyoulikeit/master/words/${d.filename}`);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":35,"value":"words = Promise.all(files.map(d=> d3.text(d)))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":37,"value":"corpus = new tfidf.Corpus(\n acts, \n words, \n useStopwords,\n customStopwords,\n K1,\n b\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":43,"value":"K1 = 2","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":45,"value":"b=0.75","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2,"value":"tfidf = import(\"tiny-tfidf\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":130,"value":"fileLookup = new Map(acts.map((d, i) => [d, files[i]]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":59,"value":"import {text} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":53,"value":"import {table} from \"@tmcw/tables/2\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":148,"value":"commonTermsTable = function(identifier1, identifier2, cutoff = 10) {\n const ctTable = table(\n corpus\n .getCommonTerms(identifier1, identifier2, cutoff)\n .map(d => ({ Term: d[0], Weight: d[1] }))\n );\n ctTable.style.maxWidth = \"200px\";\n return ctTable;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7,"value":"d3 = require('d3@5')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W35","stdlib":"1","titling":"auto"}} 30:"$2f:body"