Session 1 - Text Analysis Overview.ipynb

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    "%matplotlib inline\n",
    "\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.figsize'] = [9.0, 6.0]"
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    "# Session 1: Text Analysis Overview \n",
    "**May 31, 2018**"
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     "slide_type": "notes"
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    "In this session, we will explore the basics of applying linguistic and grammatical tools to the statistical and contextual analysis of text documents, often the preliminary steps to building text analytics models. We will discuss the creation and maintenance of a corpus, a dataset composed of text documents, and the techniques to preprocess text into units of discourse - paragraphs, sentences, and tokens with lexical models. We will then discuss morphological and contextual approaches to analyzing the contents of a corpus to get a broad overview of the significant terms and phrases it contains. This session will give us the tools we need to begin deeper syntactic and modeling analysis of text in the next sessions. Note that this session will begin with a broad overview of natural language processing, for those who are interested in the general details of text analytics and how they might influence research techniques at The Brookings Institution. "
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    "\n",
    "## Creating a Corpus \n",
    "\n",
    "The first step in doing text analytics is acquiring a text corpus. Corpus ingestion processes can range from simple to complex, but should be approached from the standpoint that corpora are living datasets that should grow and be maintained over time. "
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    "### Step One: Fetch Data \n",
    "\n",
    "There are many ways to fetch data from the Internet, from RSS feeds to data downloads (e.g. Wikipedia) to API requests. For now, we'll fetch some simple HTML from the Washington Post using the ID from their short URLs."
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    "import os \n",
    "import requests \n",
    "\n",
    "# Base Washington Post URL\n",
    "WAPO_URL = \"https://wapo.st/\"\n",
    "\n",
    "\n",
    "def fetch_wapo(sid):\n",
    "    url = WAPO_URL + sid \n",
    "    res = requests.get(url)\n",
    "    return res.text "
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border-bottom-100-pct\"> <div id=\"article-topper\" class=\"article-topper \"> <div class=\"headline-kicker\"><a class='kicker-link' href='/politics'>Politics</a></div> <div> <div id=\"topper-headline-wrapper\" class=\"col-xs-12 col-sm-12 col-lg-12\"> <h1 data-pb-field=\"custom.topperDisplayName\" itemprop=\"headline\">Judge sets endpoint for review of seized Cohen files, rebukes Avenatti’s ‘publicity tour’</h1> </div> <wp-ad id=\"slug_tiffany_tile\" data-ad-type=\"tiffany_tile\" class=\"hidden-xs right\"></wp-ad> </div> <div class=\"clear\"></div> </div> </div> </div> <div class=\"pb-container\"> </div> </section> <section id=\"main-content\" class=\"col-xl-9 col-lg-8 col-md-8 col-sm-12 col-xs-12 col-xs-offset-0 col-sm-offset-0 col-md-offset-0 col-lg-offset-0 layout\"> <div class=\"moat-trackable pb-f-theme-normal pb-f-dehydrate-false pb-f-async-false full pb-feature pb-layout-item pb-f-article-article-deck\" moat-id=\"article/article-deck\" data-chain-name=\"no-name\" 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<div class=\"posttv-video-embed powa\" data-org=\"wapo\" data-uuid=\"ce574268-427b-11e8-b2dc-b0a403e4720a\" data-ad-bar=\"1\" data-playthrough=\"1\" data-blurb=\"1\" data-object-id=\"5ad6556ce4b084a88084900e\" data-youtube-id=\"\" data-live=\"0\" data-aspect-ratio=\"0.5625\"> </div> </div> </div> </div> <script src=\"https://www.washingtonpost.com/player/prod/powaBoot.js?_=20180521B\" data-loaded-via=\"inline\" async></script> <div class=\"pb-sig-line hasnt-headshot has-0-headshots hasnt-bio is-not-column\"> <span class=\"pb-byline\" itemprop=\"author\" itemscope itemtype=\"http://schema.org/Person\"><span class=\"byline-role\">by </span><a href=\"https://www.washingtonpost.com/people/philip-bump/\"><span itemprop=\"name\">Philip Bump</span></a> and <a href=\"https://www.washingtonpost.com/people/mark-berman/\"><span itemprop=\"name\">Mark Berman</span></a></span> <span class=\"pb-timestamp\" itemprop=\"datePublished\" content=\"2018-05-30T02:37-500\">May 30</span> <span class=\"pb-tool email\"><a href=\"mailto:philip.bump@washpost.com;mark.berman@washpost.com?subject=Reader feedback for 'Judge sets endpoint for review of seized Cohen files, rebukes Avenatti’s ‘publicity tour’'\"><span class=\"fa fa-envelope\"></span><span class=\"envelope-label\">Email the author</span></a></span> <span class=\"pb-bolt\"><i class=\"fa fa-bolt\"></i></span> </div> <article itemprop=\"articleBody\" class=\"paywall\"> <p data-elm-loc=\"1\" class=\"text \"> <span class=\"dateline\">NEW YORK —</span> A federal judge on Wednesday gave attorneys for Michael Cohen, President Trump’s longtime personal lawyer, a little more than two weeks to finish reviewing material seized from his office and residences to determine what is protected by attorney-client privilege. </p> <p data-elm-loc=\"2\">The June 15 deadline was set during a hearing in Manhattan that focused on the status of these seized materials and featured sparring between Cohen’s attorneys and Michael Avenatti, an attorney for Stormy Daniels, an adult-film actress to whom Cohen paid $130,000 after she alleged she had an affair with Trump.</p> <p data-elm-loc=\"3\">Avenatti had sought to join the case but withdrew his motion after U.S. District Judge Kimba Wood warned that Avenatti would have to end his “publicity tour” if she granted him standing.</p> <p data-elm-loc=\"4\">Avenatti did not elaborate on his decision in his motion to withdraw, noting that he could refile it “if necessary, at a later time,” depending on what happens with Daniels’s motion to intervene in the case. That motion has been held pending discussions between Avenatti and federal officials, according to court records.</p> <p data-elm-loc=\"5\">Avenatti pointed to that motion, rather than the judge’s skepticism Wednesday, in explaining why he suddenly withdrew.</p> <p data-elm-loc=\"6\">“It is no longer necessary in light of the motion to intervene, which may not be necessary, being delayed,” Avenatti wrote in an email. “We will refile it when and if the motion is taken up.”</p> <div data-elm-loc=\"7\" class=\"inline-content inline-photo inline-photo-left\" style=\"width:300px;\"> <a name=\"X4VBNFTDW4I6RGOSBVTY5QEMF4\"></a> <img src=\"https://www.washingtonpost.com/resizer/TXJKur-cQ3m4AF9lqk_kkjfemkk=/3x2/www.washingtonpost.com/pb/resources/img/spacer.gif\" data-hi-res-src=\"https://www.washingtonpost.com/resizer/V3eEQCv8A4x6aMhp8-DZBjeW0zs=/1484x0/arc-anglerfish-washpost-prod-washpost.s3.amazonaws.com/public/X4VBNFTDW4I6RGOSBVTY5QEMF4.jpg\" data-low-res-src=\"https://www.washingtonpost.com/resizer/v-tikVGNHiDzo7IZNc8PRKNcSh4=/480x0/arc-anglerfish-washpost-prod-washpost.s3.amazonaws.com/public/X4VBNFTDW4I6RGOSBVTY5QEMF4.jpg\" data-raw-src=\"https://arc-anglerfish-washpost-prod-washpost.s3.amazonaws.com/public/X4VBNFTDW4I6RGOSBVTY5QEMF4.jpg\" data-threshold=\"480\" class=\"unprocessed _3-to-2 placeholder \"/><br/> <span class=\"pb-caption\">Michael Cohen in New York earlier this month. Prosecutors already have access to nearly 300,000 potential pieces of evidence seized in an April raid of Cohen’s office and residences. (Drew Angerer/Getty Images)</span> </div> <p data-elm-loc=\"8\" class=\"interstitial-link \"><i>[<a href=\"https://www.washingtonpost.com/politics/michael-avenatti-is-using-trump-tactics-to-battle-trump-some-say-that-may-hurt-his-client-stormy-daniels/2018/05/14/6ea957b2-553a-11e8-a551-5b648abe29ef_story.html?utm_term=.eb30089bedf0\">Michael Avenatti is using Trump’s tactics to battle Trump</a>]</i></p> <p data-elm-loc=\"9\">Cohen, who attended the hearing but did not speak, is being <a href=\"https://www.washingtonpost.com/politics/fbi-seizes-records-related-to-stormy-daniels-in-raid-of-trump-attorney-michael-cohens-office/2018/04/09/e3e43cf4-3c30-11e8-974f-aacd97698cef_story.html?utm_term=.30e41ea37ce2\" title=\"www.washingtonpost.com\">investigated</a> for possible bank and wire fraud and campaign finance violations as part of a probe looking at his efforts to suppress damaging stories about Trump — such as Daniels’s claims of a sexual encounter with him years earlier — just before the 2016 presidential election.</p> <p data-elm-loc=\"10\">As part of the probe, federal agents seized Cohen’s records and documents on April 9, prompting a legal fight over who would get access to the materials and when. Cohen and Trump both argued that their attorneys should get to review the materials to see what is privileged before investigators get them. Wood <a href=\"https://www.washingtonpost.com/politics/federal-prosecutors-quickly-cite-trumps-fox-and-friends-comments-to-help-make-their-case-on-cohen-documents/2018/04/26/709eeaee-4961-11e8-827e-190efaf1f1ee_story.html?utm_term=.778ee254281c\">appointed Barbara Jones</a>, a former federal judge, as special master to go over the documents before they are turned over to federal prosecutors.</p> <p data-elm-loc=\"11\">Jones wrote in a filing submitted Tuesday night that after her reviews so far, the U.S. Attorney’s Office for the Southern District of New York has been given access to more than 292,000 items, and more than 1 million items “not designated privileged or highly personal” were expected to be released Wednesday.</p> <p data-elm-loc=\"12\">Attorneys for Cohen and Trump had said many of the seized documents and records could be protected by attorney-client privilege. Jones noted that so far, only 252 items had been flagged as privileged by attorneys for Trump or Cohen in two of the four sets of material she’d reviewed, and she said she expected to make a recommendation about those materials by Monday. The number of documents flagged as privileged in the other sets, if any, is not known.</p> <p data-elm-loc=\"13\">Jones also reported having received additional electronic data over the past two weeks, including “data from a video recorder, two computers, and mobile storage devices” that “includes various video, electronic communications and documents as well as data typically associated with computer usage.”</p> <p data-elm-loc=\"14\" class=\"interstitial-link \"><i>[<a href=\"https://www.washingtonpost.com/politics/giuliani-pleased-with-his-media-tour-everybodys-reacting-to-us-now/2018/05/06/6764454e-5138-11e8-9c91-7dab596e8252_story.html?utm_term=.88d902ee3ee8\">Giuliani, Trump’s lawyer, is pleased with his media tour</a>]</i></p> <p data-elm-loc=\"15\">A significant amount of material still needs to be reviewed. In court Tuesday, Todd Harrison, an attorney for Cohen, said his team had received 3.7 million files, about 1.3 million of which had already been turned over to Jones.</p> <p data-elm-loc=\"16\">Harrison argued against a mid-June deadline, saying that “we are moving heaven and earth” to rapidly complete a privilege review. He said his firm had 17 people evaluating the materials, which came from 13 mobile devices and 19 other digital-media devices.</p> <p data-elm-loc=\"17\">“We have people who are working all night,” Harrison said. “We have people who are sleeping on couches.”</p> <p data-elm-loc=\"18\">Wood was not persuaded, dismissing Cohen’s concerns and those of an attorney for the Trump Organization who argued that a faster pace might degrade the review.</p> <p data-elm-loc=\"19\">Rachel Maimin, an assistant U.S. attorney, said her office was still trying to access files from two Blackberry devices, which Cohen believed belonged to his wife nearly a decade ago, and the contents of a shredding machine. This material, she said, would be turned over to Cohen’s team as soon as possible.</p> <p data-elm-loc=\"20\">Wood set the June 15 deadline for attorneys for Cohen, working with attorneys for Trump and his company, to determine what is privileged and turn it over to the special master. Anything not sent to Jones by that date gets handed over instead to a “taint team” of prosecutors who are not part of the investigation into Cohen and would work to determine what is covered by privilege.</p> <p data-elm-loc=\"21\">Wood also heard arguments about whether Avenatti — who represents Daniels in other lawsuits she has filed against Trump and Cohen — could join the case, providing the hearing’s main drama. Cohen’s attorneys and Avenatti have engaged in an extended back-and-forth over the question, filing dueling letters Tuesday night and then arguing in court Wednesday.</p> <p data-elm-loc=\"22\" class=\"interstitial-link \"><i>[<a href=\"https://www.washingtonpost.com/politics/lawyers-for-michael-cohen-accuse-stormy-daniels-attorney-of-distributing-false-information/2018/05/09/6d2b8214-53d4-11e8-a551-5b648abe29ef_story.html?utm_term=.21b7a671ebd8\">Michael Cohen’s lawyers accuse Avenatti of spreading false information</a>]</i></p> <p data-elm-loc=\"23\">When he entered the courtroom Wednesday, Avenatti approached the bench to find out where he should sit. He was pointed to the back of the room, where he approached Alan Futerfas, attorney for the Trump Organization.</p> <p data-elm-loc=\"24\">“I was told to pull up a seat and share this table,” Avenatti said with a chuckle before sitting down.</p> <p data-elm-loc=\"25\">Avenatti said in court that a reporter called him to describe hearing a recorded conversation between Cohen and Keith Davidson — who as an attorney for Daniels negotiated <a href=\"https://www.washingtonpost.com/news/post-nation/wp/2018/02/14/longtime-trump-attorney-says-he-made-130000-payment-to-stormy-daniels-with-his-money/\">the $130,000 payment she received from Cohen</a> before the 2016 election — and said this could only have been released by Cohen’s team. He said this was “very surprising and disturbing to me” as an attorney for Daniels, whose real name is Stephanie Clifford.</p> <p data-elm-loc=\"26\">Stephen Ryan, a Cohen attorney, said he was unaware of the release of any audio file. He focused instead on Avenatti’s <a href=\"https://www.washingtonpost.com/politics/trumps-attorney-michael-cohen-was-hired-by-us-affiliate-of-russian-company/2018/05/08/12a218c8-52f6-11e8-abd8-265bd07a9859_story.html?noredirect=on&amp;utm_term=.ba22391483d2\">disclosure earlier this month</a> of financial information related to Cohen.</p> <p data-elm-loc=\"27\">“The reason I rise today is because I’ve never seen an attorney conduct himself in the manner Mr. Avenatti has,” Ryan said. Avenatti’s motion, he said, was “sidestepping his intentionally malicious and prejudicial release of that information.”</p> <p data-elm-loc=\"28\">Ryan added, “It was a premeditated drive-by shooting of my client’s rights.”</p> <p data-elm-loc=\"29\" class=\"interstitial-link \"><i>[<a href=\"https://www.washingtonpost.com/politics/mueller-questioned-payment-to-trump-lawyer-michael-cohen/2018/05/09/6ad3a7d6-538d-11e8-a551-5b648abe29ef_story.html?utm_term=.d5f103d62b44\">‘I’m crushing it’: How Cohen, touting access to Trump, convinced companies to pay millions</a>]</i></p> <p data-elm-loc=\"30\">Avenatti defended his release of the financial information, which prompted the Treasury Department’s inspector general to <a href=\"https://www.washingtonpost.com/politics/treasury-inspector-general-launches-probe-into-possible-leak-of-michael-cohens-banking-records/2018/05/09/d33c2010-5391-11e8-b00a-17f9fda3859b_story.html\">launch an investigation</a>.</p> <p data-elm-loc=\"31\">“There’s no evidence we did anything improper,” he said. “We haven’t been contacted by the Treasury Department. We haven’t been contacted by the FBI.”</p> <p data-elm-loc=\"32\">In their letter Tuesday night, Cohen’s attorneys also pointed to a tweet from Avenatti about Cohen’s finances and to a recent <a href=\"https://www.cnbc.com/2018/05/22/firm-of-lawyer-avenatti-hit-with-10-mln-judgment-in-bankruptcy-court.html\" target=\"_blank\">bankruptcy court decision</a> against Avenatti’s law firm. Ryan said that was raised because it showed that “Mr. Avenatti cannot keep his agreements.”</p> <p data-elm-loc=\"33\">Joanna Hendon, a Trump attorney, said she shared the concerns raised by Cohen’s team about Avenatti. She said that while Avenatti argued that the firm in the bankruptcy case was not involved with Daniels’s litigation, it had sent emails regarding her case.</p> <p data-elm-loc=\"34\">In response, Avenatti noted that “Mr. Trump had his own fair share of bankruptcies” and added that the president has “tweeted about Mr. Cohen in great detail.”</p> <p data-elm-loc=\"35\">Avenatti’s enthusiasm for media attention also drew criticism from Ryan and Hendon, and Wood suggested that she shared those concerns. The judge told him that if he is allowed to participate in the process, he would “not be permitted to use this court as a platform for anything” and said he could no longer declare that Cohen is guilty of potential charges.</p> <p data-elm-loc=\"36\">If Avenatti had been given standing in the case, Wood said, he would have to “stop in its tracks your publicity tour on television and elsewhere.”</p> <p data-elm-loc=\"37\">After leaving the hearing, Avenatti offered an energetic condemnation of Cohen to the press assembled outside the courthouse. 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label-kicker\"> Most Read <span class=\"section\"> Politics</span> </div> </div> <ul class=\"skin skin-card large def-feed\"> <li class=\"pm-content \"> <a href=\"https://www.washingtonpost.com/politics/courts_law/supreme-court-rules-in-favor-of-baker-who-would-not-make-wedding-cake-for-gay-couple/2018/06/04/50c68cf8-6802-11e8-bea7-c8eb28bc52b1_story.html?tid=pm_politics_pop\"> <div class=\"number left totalShown\"> 1 </div> <img src=\"https://www.washingtonpost.com/resizer/yAFxThroW-CI5t-yu8Q6FsF7kHI=/1x1/www.washingtonpost.com/pb/resources/img/spacer.gif?t=20170517a\" data-hi-res-src=\"https://www.washingtonpost.com/rf/image_60x60/2010-2019/WashingtonPost/2018/06/04/National-Politics/Images/AFP_15L973.jpg?t=20170517a\" data-low-res-src=\"https://www.washingtonpost.com/rf/image_60x60/2010-2019/WashingtonPost/2018/06/04/National-Politics/Images/AFP_15L973.jpg?t=20170517a\" data-threshold=\"60\" width=\"60\" height=\"60\" class=\"photo right big-img _1-to-1 unprocessed placeholder\"/> <div class=\"headline \"> Supreme Court rules in favor of baker who would not make wedding cake for gay couple </div> <div class=\"clear\"></div> </a> </li> <li class=\"pm-content \"> <a href=\"https://www.washingtonpost.com/news/the-fix/wp/2018/06/04/a-kentucky-crowd-cheered-a-valedictorian-for-quoting-trump-then-he-told-them-it-was-obama/?tid=pm_politics_pop\"> <div class=\"number left totalShown\"> 2 </div> <img src=\"https://www.washingtonpost.com/resizer/yAFxThroW-CI5t-yu8Q6FsF7kHI=/1x1/www.washingtonpost.com/pb/resources/img/spacer.gif?t=20170517a\" data-hi-res-src=\"https://www.washingtonpost.com/rf/image_60x60/2010-2019/WashingtonPost/2018/06/02/National-Politics/Images/Rex_US_Coast_Guard_ChangeofCommand_9698630P-4542.jpg?t=20170517a\" data-low-res-src=\"https://www.washingtonpost.com/rf/image_60x60/2010-2019/WashingtonPost/2018/06/02/National-Politics/Images/Rex_US_Coast_Guard_ChangeofCommand_9698630P-4542.jpg?t=20170517a\" data-threshold=\"60\" width=\"60\" height=\"60\" class=\"photo right big-img _1-to-1 unprocessed placeholder\"/> <div class=\"headline \"> A Kentucky crowd cheered a valedictorian for quoting Trump. Then he told them it was Obama. </div> <div class=\"clear\"></div> </a> </li> <li class=\"pm-content \"> <a href=\"https://www.washingtonpost.com/politics/trump-says-he-has-absolute-right-to-pardon-himself-of-federal-crimes-but-denies-any-wrongdoing/2018/06/04/3d78348c-67dd-11e8-bea7-c8eb28bc52b1_story.html?tid=pm_politics_pop\"> <div class=\"number left totalShown\"> 3 </div> <img src=\"https://www.washingtonpost.com/resizer/yAFxThroW-CI5t-yu8Q6FsF7kHI=/1x1/www.washingtonpost.com/pb/resources/img/spacer.gif?t=20170517a\" data-hi-res-src=\"https://www.washingtonpost.com/rf/image_60x60/2010-2019/WashingtonPost/2018/05/02/National-Politics/Images/Botsford180501Trump26071.jpg?t=20170517a\" data-low-res-src=\"https://www.washingtonpost.com/rf/image_60x60/2010-2019/WashingtonPost/2018/05/02/National-Politics/Images/Botsford180501Trump26071.jpg?t=20170517a\" data-threshold=\"60\" width=\"60\" height=\"60\" class=\"photo right big-img _1-to-1 unprocessed placeholder\"/> <div class=\"headline \"> Trump says he has ‘absolute right’ to pardon himself of federal crimes but denies any wrongdoing </div> <div class=\"clear\"></div> </a> </li> <li class=\"pm-content \"> <a href=\"https://www.washingtonpost.com/politics/courts_law/supreme-court-throws-out-lower-court-decision-that-allowed-immigrant-teenager-to-obtain-abortion/2018/06/04/50a59ba4-67f5-11e8-bea7-c8eb28bc52b1_story.html?tid=pm_politics_pop\"> <div class=\"number left totalShown\"> 4 </div> <img src=\"https://www.washingtonpost.com/resizer/yAFxThroW-CI5t-yu8Q6FsF7kHI=/1x1/www.washingtonpost.com/pb/resources/img/spacer.gif?t=20170517a\" data-hi-res-src=\"https://www.washingtonpost.com/rf/image_60x60/2010-2019/WashingtonPost/2017/09/12/Editorial-Opinion/Images/court04-2338.jpg?t=20170517a\" data-low-res-src=\"https://www.washingtonpost.com/rf/image_60x60/2010-2019/WashingtonPost/2017/09/12/Editorial-Opinion/Images/court04-2338.jpg?t=20170517a\" data-threshold=\"60\" width=\"60\" height=\"60\" class=\"photo right big-img _1-to-1 unprocessed placeholder\"/> <div class=\"headline \"> Supreme Court throws out lower-court decision that allowed immigrant teen to obtain abortion </div> <div class=\"clear\"></div> </a> </li> <li class=\"pm-content last\"> <a href=\"https://www.washingtonpost.com/news/powerpost/paloma/daily-202/2018/06/04/daily-202-cops-are-called-when-a-senator-tries-to-see-kids-taken-from-their-immigrant-parents/5b1486fe30fb04092c75ae42/?tid=pm_politics_pop\"> <div class=\"number left totalShown\"> 5 </div> <img src=\"https://www.washingtonpost.com/resizer/yAFxThroW-CI5t-yu8Q6FsF7kHI=/1x1/www.washingtonpost.com/pb/resources/img/spacer.gif?t=20170517a\" data-hi-res-src=\"https://www.washingtonpost.com/wp-apps/imrs.php?src=https://palomaimages.washingtonpost.com/pr2/ea3d44a72e7a25d45e2ea48739a7ebb2-640-360-70-8-merkleyfacebookjune3_tstmp_1528114816.jpg&amp;w=60&amp;h=60&t=20170517a\" data-low-res-src=\"https://www.washingtonpost.com/wp-apps/imrs.php?src=https://palomaimages.washingtonpost.com/pr2/ea3d44a72e7a25d45e2ea48739a7ebb2-640-360-70-8-merkleyfacebookjune3_tstmp_1528114816.jpg&amp;w=60&amp;h=60&t=20170517a\" data-threshold=\"60\" width=\"60\" height=\"60\" class=\"photo right big-img _1-to-1 unprocessed placeholder\"/> <div class=\"headline \"> <span class=\"transparency-label\"> Analysis </span> The Daily 202: Cops are called when a senator tries to see kids taken from their immigrant parents </div> <div class=\"clear\"></div> </a> </li> <li class=\"pm-addl-content hide \"> <a href=\"https://www.washingtonpost.com/opinions/the-supreme-courts-narrow-ruling-on-a-wedding-cake-is-a-step-in-the-right-direction/2018/06/04/8b1fcc32-6813-11e8-bea7-c8eb28bc52b1_story.html?tid=pm_opinions_pop\"> <div class=\"number left trending\"> <i class=\"fa fa-trending-arrow\"></i> </div> <img src=\"https://www.washingtonpost.com/resizer/yAFxThroW-CI5t-yu8Q6FsF7kHI=/1x1/www.washingtonpost.com/pb/resources/img/spacer.gif?t=20170517a\" data-hi-res-src=\"https://www.washingtonpost.com/rf/image_60x60/2010-2019/WashingtonPost/2017/06/28/National-Politics/Images/JackPhillips_Staver_0291472788985.jpg?t=20170517a\" data-low-res-src=\"https://www.washingtonpost.com/rf/image_60x60/2010-2019/WashingtonPost/2017/06/28/National-Politics/Images/JackPhillips_Staver_0291472788985.jpg?t=20170517a\" data-threshold=\"60\" width=\"60\" height=\"60\" class=\"photo right big-img _1-to-1 unprocessed placeholder\"/> <div class=\"headline \"> <span class=\"transparency-label\"> Opinion </span> The Supreme Court’s narrow ruling on a wedding cake is a step in the right direction </div> <div class=\"clear\"></div> </a> </li> <li class=\"pm-addl-content hide last\"> <a href=\"https://www.washingtonpost.com/news/global-opinions/wp/2018/06/04/trumps-ambassador-to-germany-is-making-a-fool-out-of-himself-and-the-u-s/?tid=pm_opinions_pop\"> <div class=\"number left trending\"> <i class=\"fa fa-trending-arrow\"></i> </div> <img src=\"https://www.washingtonpost.com/resizer/yAFxThroW-CI5t-yu8Q6FsF7kHI=/1x1/www.washingtonpost.com/pb/resources/img/spacer.gif?t=20170517a\" data-hi-res-src=\"https://www.washingtonpost.com/rf/image_60x60/2010-2019/Wires/Images/2018-06-04/Getty/AFP_15L882.jpg?t=20170517a\" data-low-res-src=\"https://www.washingtonpost.com/rf/image_60x60/2010-2019/Wires/Images/2018-06-04/Getty/AFP_15L882.jpg?t=20170517a\" data-threshold=\"60\" width=\"60\" height=\"60\" class=\"photo right big-img _1-to-1 unprocessed placeholder\"/> <div class=\"headline \"> <span class=\"transparency-label\"> Opinion </span> Trump&#8217;s ambassador to Germany is making a fool out of himself &#8212; and the U.S. </div> <div class=\"clear\"></div> </a> </li> </ul> 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      "                    s.setAttribute(\"src\", scripts[i].src);\n",
      "                    d.body.appendChild(s);\n",
      "                }\n",
      "                else {\n",
      "                    window.pageBuilder._executeAsyncFeatureScripts(scripts[i].innerHTML); //eval isn't evil always.\n",
      "                }\n",
      "            }\n",
      "            for(var file in resources) {\n",
      "                if(resources.hasOwnProperty(file) && file.endsWith(\".js\")) {\n",
      "                    var s1 = d.createElement(\"script\");\n",
      "                    s1.setAttribute(\"src\", resources[file]);\n",
      "                    d.body.appendChild(s1);\n",
      "                }\n",
      "            }\n",
      "            window.pageBuilder.featureLoaded(element);\n",
      "        }\n",
      "        var url = _context + \"/api/v2/render/feature/\"+id+\"?rid=\"+_rid+\"&contentUri=\"+_uri;\n",
      "        window.pageBuilder._getJson(url, function(obj) { injectFeatureDom(id, obj); });\n",
      "    };\n",
      "\n",
      "window.pageBuilder.loadAsyncChain = window.pageBuilder.loadAsyncChain || function (id) {\n",
      "        var d = window.document;\n",
      "        function injectChainDom(id, apiResponse) {\n",
      "            var element = d.getElementById(id);\n",
      "            var resources = apiResponse.pageResources || {};\n",
      "            element.outerHTML = apiResponse.rendering;\n",
      "            element = d.getElementById(id); //update the element\n",
      "            var scripts = Array.prototype.slice.call(element.getElementsByTagName(\"script\"));\n",
      "            for (var i = 0; i < scripts.length; i++) {\n",
      "                if (scripts[i].src != \"\") {\n",
      "                    var s = d.createElement(\"script\");\n",
      "                    s.setAttribute(\"src\", scripts[i].src);\n",
      "                    d.body.appendChild(s);\n",
      "                }\n",
      "                else {\n",
      "                    window.pageBuilder._executeAsyncFeatureScripts(scripts[i].innerHTML); //eval isn't evil always.\n",
      "                }\n",
      "            }\n",
      "            for(var file in resources) {\n",
      "                if(resources.hasOwnProperty(file) && file.endsWith(\".js\")) {\n",
      "                    var s1 = d.createElement(\"script\");\n",
      "                    s1.setAttribute(\"src\", resources[file]);\n",
      "                    d.body.appendChild(s1);\n",
      "                }\n",
      "            }\n",
      "            window.pageBuilder.chainLoaded(element,apiResponse);\n",
      "        }\n",
      "        var url = _context + \"/api/v2/render/chain/\"+id+\"?rid=\"+_rid+\"&contentUri=\"+_uri;\n",
      "        window.pageBuilder._getJson(url, function(obj) { injectChainDom(id, obj); });\n",
      "    };\n",
      "\n",
      "window.pageBuilder.loadAsyncFeatures = window.pageBuilder.loadAsyncFeatures || function () {\n",
      "        var d = window.document;\n",
      "        var scriptsToAdd = [];\n",
      "        var featuresLoaded = [];\n",
      "        function injectAsyncFeatures(apiResp){\n",
      "            for(var fid in apiResp){\n",
      "                if (!apiResp.hasOwnProperty(fid)) continue;\n",
      "\n",
      "                var element = d.getElementById(fid);\n",
      "                var resources = apiResp[fid].pageResources || {};\n",
      "                element.outerHTML = apiResp[fid].rendering;\n",
      "                element = d.getElementById(fid); //update the element\n",
      "                var scripts = Array.prototype.slice.call(element.getElementsByTagName(\"script\"));\n",
      "                for (var i = 0; i < scripts.length; i++) {\n",
      "                    if (scripts[i].src != \"\") {\n",
      "                        var s = d.createElement(\"script\");\n",
      "                        s.setAttribute(\"src\", scripts[i].src);\n",
      "                        d.body.appendChild(s);\n",
      "                    }\n",
      "                    else {\n",
      "                        window.pageBuilder._executeAsyncFeatureScripts(scripts[i].innerHTML); //eval isn't evil always.\n",
      "                    }\n",
      "                }\n",
      "                for(var file in resources) {\n",
      "                    if(resources.hasOwnProperty(file) && file.endsWith(\".js\")) {\n",
      "                        resources[file].forEach(function(src){\n",
      "                            if (scriptsToAdd.indexOf(src) === -1) { scriptsToAdd.push(src) }\n",
      "                        });\n",
      "                    }\n",
      "                }\n",
      "                featuresLoaded.push(element);\n",
      "            }\n",
      "            scriptsToAdd.forEach(function (src) {\n",
      "                var s = d.createElement(\"script\");\n",
      "                s.setAttribute(\"src\", src);\n",
      "                d.body.appendChild(s);\n",
      "            });\n",
      "            featuresLoaded.forEach(function(element){\n",
      "                window.pageBuilder.featureLoaded(element);\n",
      "            });\n",
      "        }\n",
      "    var url = _context + \"/api/v2/async/features?rid=\"+_rid+\"&contentUri=\"+_uri;\n",
      "    window.pageBuilder._getJson(url, function(resp) { injectAsyncFeatures(resp)});\n",
      "};\n",
      "window.pageBuilder.loadAsyncChains = window.pageBuilder.loadAsyncChains || function () {\n",
      "        var d = window.document;\n",
      "        var chains = d.getElementsByClassName(\"pb-async pb-chain\");\n",
      "        var ids = [].slice.call(chains).map(function (node) { return node.id; });\n",
      "        ids.forEach(function(id) {\n",
      "            window.pageBuilder.loadAsyncChain(id);\n",
      "        });\n",
      "    };\n",
      "\n",
      "window.pageBuilder.loadAsync = window.pageBuilder.loadAsync || function(){\n",
      "        window.pageBuilder.loadAsyncFeatures();\n",
      "        window.pageBuilder.loadAsyncChains();\n",
      "};\n",
      "\n",
      "window.pageBuilder.loadAsync();\n",
      "\n",
      "</script>\n"
     ]
    }
   ],
   "source": [
    "story = fetch_wapo(sid=\"2H0Yc36\")\n",
    "print(story)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Step Two: Data Storage \n",
    "\n",
    "Constructing a corpus on disk is as simple as saving individual documents to a root corpus directory, stored in subdirectories which represent categories or classes. The goal is something like:\n",
    "\n",
    "```\n",
    "data/corpus/\n",
    "├── README\n",
    "├── feeds.json\n",
    "├── manifest.csv\n",
    "└── business\n",
    "|   ├── 56d625fac1808113ffb877f5.pickle\n",
    "|   ├── 56d70ab5c180810560aeb7bc.pickle\n",
    "└── news\n",
    "|   ├── 56d62554c1808113ffb87492.pickle\n",
    "|   ├── 56d6255dc1808113ffb874f0.pickle\n",
    "└── politics\n",
    "|   ├── 56d6292ac1808113ffb87c28.pickle\n",
    "|   ├── 56d62931c1808113ffb87c33.pickle\n",
    "└── sports\n",
    "|   ├── 56d62adec1808113ffb88054.pickle\n",
    "|   ├── 56d70f17c180810560aec345.pickle\n",
    "└── tech\n",
    "    ├── 56d71b95c180810545e601b7.pickle\n",
    "    ├── 56d79efcc18081097fdb29b3.pickle\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Root directory of our corpus\n",
    "DATA = os.path.join(\"data\", \"wapo\")\n",
    "\n",
    "\n",
    "def save_wapo(sid, category=\"politics\"):\n",
    "    dpath = os.path.join(DATA, category)\n",
    "    if not os.path.exists(dpath):\n",
    "        os.makedirs(dpath)\n",
    "    \n",
    "    path = os.path.join(dpath, sid + \".html\")\n",
    "    with open(path, 'w') as f:\n",
    "        f.write(fetch_wapo(sid)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "save_wapo(\"2H0Yc36\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Step Three: Clean Up \n",
    "\n",
    "Rich content displayed with HTML has a huge amount of information that we do not need for our analytics: navigation, advertisements, and markup not related to the text. We need to extract only the part of the document we're interested in analyzing. \n",
    "\n",
    "Often the extraction is targeted specifically to the documents we've ingested. Larger structures such as chapters, sections or paragraphs can be further extracted using the markup or other structural features (e.g. extracting the comments from the bottom of a blog post)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  NEW YORK — A federal judge on Wednesday gave attorneys for Michael Cohen, President Trump’s longtime personal lawyer, a little more than two weeks to finish reviewing material seized from his office and residences to determine what is protected by attorney-client privilege.  The June 15 deadline was set during a hearing in Manhattan that focused on the status of these seized materials and featured sparring between Cohen’s attorneys and Michael Avenatti, an attorney for Stormy Daniels, an adult-film actress to whom Cohen paid $130,000 after she alleged she had an affair with Trump. Avenatti had sought to join the case but withdrew his motion after U.S. District Judge Kimba Wood warned that Avenatti would have to end his “publicity tour” if she granted him standing. Avenatti did not elaborate on his decision in his motion to withdraw, noting that he could refile it “if necessary, at a later time,” depending on what happens with Daniels’s motion to intervene in the case. That motion has been held pending discussions between Avenatti and federal officials, according to court records. Avenatti pointed to that motion, rather than the judge’s skepticism Wednesday, in explaining why he suddenly withdrew. “It is no longer necessary in light of the motion to intervene, which may not be necessary, being delayed,” Avenatti wrote in an email. “We will refile it when and if the motion is taken up.”    Michael Cohen in New York earlier this month. Prosecutors already have access to nearly 300,000 potential pieces of evidence seized in an April raid of Cohen’s office and residences. (Drew Angerer/Getty Images)  [Michael Avenatti is using Trump’s tactics to battle Trump] Cohen, who attended the hearing but did not speak, is being investigated for possible bank and wire fraud and campaign finance violations as part of a probe looking at his efforts to suppress damaging stories about Trump — such as Daniels’s claims of a sexual encounter with him years earlier — just before the 2016 presidential election. As part of the probe, federal agents seized Cohen’s records and documents on April 9, prompting a legal fight over who would get access to the materials and when. Cohen and Trump both argued that their attorneys should get to review the materials to see what is privileged before investigators get them. Wood appointed Barbara Jones, a former federal judge, as special master to go over the documents before they are turned over to federal prosecutors. Jones wrote in a filing submitted Tuesday night that after her reviews so far, the U.S. Attorney’s Office for the Southern District of New York has been given access to more than 292,000 items, and more than 1 million items “not designated privileged or highly personal” were expected to be released Wednesday. Attorneys for Cohen and Trump had said many of the seized documents and records could be protected by attorney-client privilege. Jones noted that so far, only 252 items had been flagged as privileged by attorneys for Trump or Cohen in two of the four sets of material she’d reviewed, and she said she expected to make a recommendation about those materials by Monday. The number of documents flagged as privileged in the other sets, if any, is not known. Jones also reported having received additional electronic data over the past two weeks, including “data from a video recorder, two computers, and mobile storage devices” that “includes various video, electronic communications and documents as well as data typically associated with computer usage.” [Giuliani, Trump’s lawyer, is pleased with his media tour] A significant amount of material still needs to be reviewed. In court Tuesday, Todd Harrison, an attorney for Cohen, said his team had received 3.7 million files, about 1.3 million of which had already been turned over to Jones. Harrison argued against a mid-June deadline, saying that “we are moving heaven and earth” to rapidly complete a privilege review. He said his firm had 17 people evaluating the materials, which came from 13 mobile devices and 19 other digital-media devices. “We have people who are working all night,” Harrison said. “We have people who are sleeping on couches.” Wood was not persuaded, dismissing Cohen’s concerns and those of an attorney for the Trump Organization who argued that a faster pace might degrade the review. Rachel Maimin, an assistant U.S. attorney, said her office was still trying to access files from two Blackberry devices, which Cohen believed belonged to his wife nearly a decade ago, and the contents of a shredding machine. This material, she said, would be turned over to Cohen’s team as soon as possible. Wood set the June 15 deadline for attorneys for Cohen, working with attorneys for Trump and his company, to determine what is privileged and turn it over to the special master. Anything not sent to Jones by that date gets handed over instead to a “taint team” of prosecutors who are not part of the investigation into Cohen and would work to determine what is covered by privilege. Wood also heard arguments about whether Avenatti — who represents Daniels in other lawsuits she has filed against Trump and Cohen — could join the case, providing the hearing’s main drama. Cohen’s attorneys and Avenatti have engaged in an extended back-and-forth over the question, filing dueling letters Tuesday night and then arguing in court Wednesday. [Michael Cohen’s lawyers accuse Avenatti of spreading false information] When he entered the courtroom Wednesday, Avenatti approached the bench to find out where he should sit. He was pointed to the back of the room, where he approached Alan Futerfas, attorney for the Trump Organization. “I was told to pull up a seat and share this table,” Avenatti said with a chuckle before sitting down. Avenatti said in court that a reporter called him to describe hearing a recorded conversation between Cohen and Keith Davidson — who as an attorney for Daniels negotiated the $130,000 payment she received from Cohen before the 2016 election — and said this could only have been released by Cohen’s team. He said this was “very surprising and disturbing to me” as an attorney for Daniels, whose real name is Stephanie Clifford. Stephen Ryan, a Cohen attorney, said he was unaware of the release of any audio file. He focused instead on Avenatti’s disclosure earlier this month of financial information related to Cohen. “The reason I rise today is because I’ve never seen an attorney conduct himself in the manner Mr. Avenatti has,” Ryan said. Avenatti’s motion, he said, was “sidestepping his intentionally malicious and prejudicial release of that information.” Ryan added, “It was a premeditated drive-by shooting of my client’s rights.” [‘I’m crushing it’: How Cohen, touting access to Trump, convinced companies to pay millions] Avenatti defended his release of the financial information, which prompted the Treasury Department’s inspector general to launch an investigation. “There’s no evidence we did anything improper,” he said. “We haven’t been contacted by the Treasury Department. We haven’t been contacted by the FBI.” In their letter Tuesday night, Cohen’s attorneys also pointed to a tweet from Avenatti about Cohen’s finances and to a recent bankruptcy court decision against Avenatti’s law firm. Ryan said that was raised because it showed that “Mr. Avenatti cannot keep his agreements.” Joanna Hendon, a Trump attorney, said she shared the concerns raised by Cohen’s team about Avenatti. She said that while Avenatti argued that the firm in the bankruptcy case was not involved with Daniels’s litigation, it had sent emails regarding her case. In response, Avenatti noted that “Mr. Trump had his own fair share of bankruptcies” and added that the president has “tweeted about Mr. Cohen in great detail.” Avenatti’s enthusiasm for media attention also drew criticism from Ryan and Hendon, and Wood suggested that she shared those concerns. The judge told him that if he is allowed to participate in the process, he would “not be permitted to use this court as a platform for anything” and said he could no longer declare that Cohen is guilty of potential charges. If Avenatti had been given standing in the case, Wood said, he would have to “stop in its tracks your publicity tour on television and elsewhere.” After leaving the hearing, Avenatti offered an energetic condemnation of Cohen to the press assembled outside the courthouse. He withdrew his motion to join a short time later.  Berman reported from Washington.   \n"
     ]
    }
   ],
   "source": [
    "from bs4 import BeautifulSoup \n",
    "from readability.readability import Document \n",
    "\n",
    "\n",
    "def extract(html):\n",
    "    article = Document(html).summary() \n",
    "    soup = BeautifulSoup(article, 'lxml')\n",
    "    return soup.get_text()\n",
    "\n",
    "\n",
    "story = extract(story)\n",
    "print(story)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Other Data Extraction \n",
    "\n",
    "Keep in mind that the raw HTML should be mined for other document meta data especially:\n",
    "\n",
    "- title \n",
    "- publication date \n",
    "- authorship \n",
    "- provenance \n",
    "- related documents (links)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Maintenance \n",
    "\n",
    "![Corpus Growth over Time](figures/s1_growing_corpora.png)\n",
    "\n",
    "Fair warning, corpora can get big as they grow over time; it's useful to strategize in advance how to maintain and organize corpora (and when to move to a database or other data management system). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Preprocessing and Tokenization\n",
    "\n",
    "![Preprocessing and Tokenization](figures/s1_preprocessing.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Discourse \n",
    "\n",
    "![Discourse](figures/s1_discourse.png)\n",
    "\n",
    "**Goal: Break down documents into units of related information.** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Segmentation \n",
    "\n",
    "Breaking larger text into sentences or utterances that contain a single thought. Not as simple as splitting on punctuation! \n",
    "\n",
    "> \"I'm glad that's finally over!\" said Mr. Smith, after receiving his Ph.d.\n",
    "\n",
    "We will use NLTK's Punkt model to segment text, trained on English sentences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  NEW YORK — A federal judge on Wednesday gave attorneys for Michael Cohen, President Trump’s longtime personal lawyer, a little more than two weeks to finish reviewing material seized from his office and residences to determine what is protected by attorney-client privilege. \n",
      "\n",
      "The June 15 deadline was set during a hearing in Manhattan that focused on the status of these seized materials and featured sparring between Cohen’s attorneys and Michael Avenatti, an attorney for Stormy Daniels, an adult-film actress to whom Cohen paid $130,000 after she alleged she had an affair with Trump. \n",
      "\n",
      "Avenatti had sought to join the case but withdrew his motion after U.S. District Judge Kimba Wood warned that Avenatti would have to end his “publicity tour” if she granted him standing. \n",
      "\n",
      "Avenatti did not elaborate on his decision in his motion to withdraw, noting that he could refile it “if necessary, at a later time,” depending on what happens with Daniels’s motion to intervene in the case. \n",
      "\n",
      "That motion has been held pending discussions between Avenatti and federal officials, according to court records. \n",
      "\n",
      "Avenatti pointed to that motion, rather than the judge’s skepticism Wednesday, in explaining why he suddenly withdrew. \n",
      "\n",
      "“It is no longer necessary in light of the motion to intervene, which may not be necessary, being delayed,” Avenatti wrote in an email. \n",
      "\n",
      "“We will refile it when and if the motion is taken up.”    Michael Cohen in New York earlier this month. \n",
      "\n",
      "Prosecutors already have access to nearly 300,000 potential pieces of evidence seized in an April raid of Cohen’s office and residences. \n",
      "\n",
      "(Drew Angerer/Getty Images)  [Michael Avenatti is using Trump’s tactics to battle Trump] Cohen, who attended the hearing but did not speak, is being investigated for possible bank and wire fraud and campaign finance violations as part of a probe looking at his efforts to suppress damaging stories about Trump — such as Daniels’s claims of a sexual encounter with him years earlier — just before the 2016 presidential election. \n",
      "\n",
      "As part of the probe, federal agents seized Cohen’s records and documents on April 9, prompting a legal fight over who would get access to the materials and when. \n",
      "\n",
      "Cohen and Trump both argued that their attorneys should get to review the materials to see what is privileged before investigators get them. \n",
      "\n",
      "Wood appointed Barbara Jones, a former federal judge, as special master to go over the documents before they are turned over to federal prosecutors. \n",
      "\n",
      "Jones wrote in a filing submitted Tuesday night that after her reviews so far, the U.S. Attorney’s Office for the Southern District of New York has been given access to more than 292,000 items, and more than 1 million items “not designated privileged or highly personal” were expected to be released Wednesday. \n",
      "\n",
      "Attorneys for Cohen and Trump had said many of the seized documents and records could be protected by attorney-client privilege. \n",
      "\n",
      "Jones noted that so far, only 252 items had been flagged as privileged by attorneys for Trump or Cohen in two of the four sets of material she’d reviewed, and she said she expected to make a recommendation about those materials by Monday. \n",
      "\n",
      "The number of documents flagged as privileged in the other sets, if any, is not known. \n",
      "\n",
      "Jones also reported having received additional electronic data over the past two weeks, including “data from a video recorder, two computers, and mobile storage devices” that “includes various video, electronic communications and documents as well as data typically associated with computer usage.” [Giuliani, Trump’s lawyer, is pleased with his media tour] A significant amount of material still needs to be reviewed. \n",
      "\n",
      "In court Tuesday, Todd Harrison, an attorney for Cohen, said his team had received 3.7 million files, about 1.3 million of which had already been turned over to Jones. \n",
      "\n",
      "Harrison argued against a mid-June deadline, saying that “we are moving heaven and earth” to rapidly complete a privilege review. \n",
      "\n",
      "He said his firm had 17 people evaluating the materials, which came from 13 mobile devices and 19 other digital-media devices. \n",
      "\n",
      "“We have people who are working all night,” Harrison said. \n",
      "\n",
      "“We have people who are sleeping on couches.” Wood was not persuaded, dismissing Cohen’s concerns and those of an attorney for the Trump Organization who argued that a faster pace might degrade the review. \n",
      "\n",
      "Rachel Maimin, an assistant U.S. attorney, said her office was still trying to access files from two Blackberry devices, which Cohen believed belonged to his wife nearly a decade ago, and the contents of a shredding machine. \n",
      "\n",
      "This material, she said, would be turned over to Cohen’s team as soon as possible. \n",
      "\n",
      "Wood set the June 15 deadline for attorneys for Cohen, working with attorneys for Trump and his company, to determine what is privileged and turn it over to the special master. \n",
      "\n",
      "Anything not sent to Jones by that date gets handed over instead to a “taint team” of prosecutors who are not part of the investigation into Cohen and would work to determine what is covered by privilege. \n",
      "\n",
      "Wood also heard arguments about whether Avenatti — who represents Daniels in other lawsuits she has filed against Trump and Cohen — could join the case, providing the hearing’s main drama. \n",
      "\n",
      "Cohen’s attorneys and Avenatti have engaged in an extended back-and-forth over the question, filing dueling letters Tuesday night and then arguing in court Wednesday. \n",
      "\n",
      "[Michael Cohen’s lawyers accuse Avenatti of spreading false information] When he entered the courtroom Wednesday, Avenatti approached the bench to find out where he should sit. \n",
      "\n",
      "He was pointed to the back of the room, where he approached Alan Futerfas, attorney for the Trump Organization. \n",
      "\n",
      "“I was told to pull up a seat and share this table,” Avenatti said with a chuckle before sitting down. \n",
      "\n",
      "Avenatti said in court that a reporter called him to describe hearing a recorded conversation between Cohen and Keith Davidson — who as an attorney for Daniels negotiated the $130,000 payment she received from Cohen before the 2016 election — and said this could only have been released by Cohen’s team. \n",
      "\n",
      "He said this was “very surprising and disturbing to me” as an attorney for Daniels, whose real name is Stephanie Clifford. \n",
      "\n",
      "Stephen Ryan, a Cohen attorney, said he was unaware of the release of any audio file. \n",
      "\n",
      "He focused instead on Avenatti’s disclosure earlier this month of financial information related to Cohen. \n",
      "\n",
      "“The reason I rise today is because I’ve never seen an attorney conduct himself in the manner Mr. Avenatti has,” Ryan said. \n",
      "\n",
      "Avenatti’s motion, he said, was “sidestepping his intentionally malicious and prejudicial release of that information.” Ryan added, “It was a premeditated drive-by shooting of my client’s rights.” [‘I’m crushing it’: How Cohen, touting access to Trump, convinced companies to pay millions] Avenatti defended his release of the financial information, which prompted the Treasury Department’s inspector general to launch an investigation. \n",
      "\n",
      "“There’s no evidence we did anything improper,” he said. \n",
      "\n",
      "“We haven’t been contacted by the Treasury Department. \n",
      "\n",
      "We haven’t been contacted by the FBI.” In their letter Tuesday night, Cohen’s attorneys also pointed to a tweet from Avenatti about Cohen’s finances and to a recent bankruptcy court decision against Avenatti’s law firm. \n",
      "\n",
      "Ryan said that was raised because it showed that “Mr. \n",
      "\n",
      "Avenatti cannot keep his agreements.” Joanna Hendon, a Trump attorney, said she shared the concerns raised by Cohen’s team about Avenatti. \n",
      "\n",
      "She said that while Avenatti argued that the firm in the bankruptcy case was not involved with Daniels’s litigation, it had sent emails regarding her case. \n",
      "\n",
      "In response, Avenatti noted that “Mr. \n",
      "\n",
      "Trump had his own fair share of bankruptcies” and added that the president has “tweeted about Mr. Cohen in great detail.” Avenatti’s enthusiasm for media attention also drew criticism from Ryan and Hendon, and Wood suggested that she shared those concerns. \n",
      "\n",
      "The judge told him that if he is allowed to participate in the process, he would “not be permitted to use this court as a platform for anything” and said he could no longer declare that Cohen is guilty of potential charges. \n",
      "\n",
      "If Avenatti had been given standing in the case, Wood said, he would have to “stop in its tracks your publicity tour on television and elsewhere.” After leaving the hearing, Avenatti offered an energetic condemnation of Cohen to the press assembled outside the courthouse. \n",
      "\n",
      "He withdrew his motion to join a short time later. \n",
      "\n",
      "Berman reported from Washington. \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from nltk import sent_tokenize \n",
    "\n",
    "for sent in sent_tokenize(story):\n",
    "    print(sent, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Tokenization \n",
    "\n",
    "Breaking a sentence into tokens that each represent a single concept. \n",
    "\n",
    "> She didn't find the book counter-intuitive (or so she said). \n",
    "\n",
    "Again, we'll use a model trained on English text to tokenize our sentences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['She', 'did', \"n't\", 'find', 'the', 'book', 'counter-intuitive', '(', 'or', 'so', 'she', 'said', ')', '.']\n"
     ]
    }
   ],
   "source": [
    "from nltk import word_tokenize \n",
    "\n",
    "\n",
    "sent = \"She didn't find the book counter-intuitive (or so she said).\"\n",
    "print(list(word_tokenize(sent)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Part of Speech Tagging \n",
    "\n",
    "Assign a part of speech (e.g. Noun, Verb, Adverb, Adjective, etc.) to each token based on their use and position in the sentence. \n",
    "\n",
    "This is a classification problem that uses context and position to make decisions; again we'll use a pre-trained model on English, this time from the Penn Treebank. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word               PoS\n",
      "-----------------  -----\n",
      "She                PRP\n",
      "did                VBD\n",
      "n't                RB\n",
      "find               VB\n",
      "the                DT\n",
      "book               NN\n",
      "counter-intuitive  JJ\n",
      "(                  (\n",
      "or                 CC\n",
      "so                 RB\n",
      "she                PRP\n",
      "said               VBD\n",
      ")                  )\n",
      ".                  .\n"
     ]
    }
   ],
   "source": [
    "from nltk import pos_tag \n",
    "from tabulate import tabulate\n",
    "\n",
    "tagged = list(pos_tag(word_tokenize(sent)))    \n",
    "print(tabulate(tagged, tablefmt='fancy', headers=['Word', 'PoS']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Putting it All Together \n",
    "\n",
    "We'll preprocess the documents we've saved to disk and store them as pickle files - Python objects such that a document is a list of sentences, which are themselves lists of `(token, tag)` tuples. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import pickle \n",
    "\n",
    "\n",
    "def preprocess(path):\n",
    "    # Relative lookup from the data directory \n",
    "    path = os.path.join(DATA, path)\n",
    "    \n",
    "    # Read the data from the input file \n",
    "    with open(path, 'r') as f:\n",
    "        data = f.read() \n",
    "    \n",
    "    # Extract the text from the HTML \n",
    "    data = extract(data)\n",
    "    \n",
    "    # Tokenize and tag the data \n",
    "    data = [\n",
    "        [\n",
    "            (token, tag) for token, tag in \n",
    "            pos_tag(word_tokenize(sent))\n",
    "        ]\n",
    "        for sent in sent_tokenize(data)\n",
    "    ]\n",
    "    \n",
    "    # Save the data as a pickle file to disk \n",
    "    path, _ = os.path.splitext(path)\n",
    "    path += \".pickle\"\n",
    "    \n",
    "    with open(path, 'wb') as f:\n",
    "        pickle.dump(data, f)\n",
    "    \n",
    "\n",
    "preprocess(\"politics/2H0Yc36.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[('NEW', 'NNP'),\n",
       "  ('YORK', 'NNP'),\n",
       "  ('—', 'VBD'),\n",
       "  ('A', 'DT'),\n",
       "  ('federal', 'JJ'),\n",
       "  ('judge', 'NN'),\n",
       "  ('on', 'IN'),\n",
       "  ('Wednesday', 'NNP'),\n",
       "  ('gave', 'VBD'),\n",
       "  ('attorneys', 'NNS'),\n",
       "  ('for', 'IN'),\n",
       "  ('Michael', 'NNP'),\n",
       "  ('Cohen', 'NNP'),\n",
       "  (',', ','),\n",
       "  ('President', 'NNP'),\n",
       "  ('Trump', 'NNP'),\n",
       "  ('’', 'NNP'),\n",
       "  ('s', 'NN'),\n",
       "  ('longtime', 'JJ'),\n",
       "  ('personal', 'JJ'),\n",
       "  ('lawyer', 'NN'),\n",
       "  (',', ','),\n",
       "  ('a', 'DT'),\n",
       "  ('little', 'RB'),\n",
       "  ('more', 'JJR'),\n",
       "  ('than', 'IN'),\n",
       "  ('two', 'CD'),\n",
       "  ('weeks', 'NNS'),\n",
       "  ('to', 'TO'),\n",
       "  ('finish', 'VB'),\n",
       "  ('reviewing', 'VBG'),\n",
       "  ('material', 'JJ'),\n",
       "  ('seized', 'VBN'),\n",
       "  ('from', 'IN'),\n",
       "  ('his', 'PRP$'),\n",
       "  ('office', 'NN'),\n",
       "  ('and', 'CC'),\n",
       "  ('residences', 'NNS'),\n",
       "  ('to', 'TO'),\n",
       "  ('determine', 'VB'),\n",
       "  ('what', 'WP'),\n",
       "  ('is', 'VBZ'),\n",
       "  ('protected', 'VBN'),\n",
       "  ('by', 'IN'),\n",
       "  ('attorney-client', 'JJ'),\n",
       "  ('privilege', 'NN'),\n",
       "  ('.', '.')],\n",
       " [('The', 'DT'),\n",
       "  ('June', 'NNP'),\n",
       "  ('15', 'CD'),\n",
       "  ('deadline', 'NN'),\n",
       "  ('was', 'VBD'),\n",
       "  ('set', 'VBN'),\n",
       "  ('during', 'IN'),\n",
       "  ('a', 'DT'),\n",
       "  ('hearing', 'NN'),\n",
       "  ('in', 'IN'),\n",
       "  ('Manhattan', 'NNP'),\n",
       "  ('that', 'WDT'),\n",
       "  ('focused', 'VBD'),\n",
       "  ('on', 'IN'),\n",
       "  ('the', 'DT'),\n",
       "  ('status', 'NN'),\n",
       "  ('of', 'IN'),\n",
       "  ('these', 'DT'),\n",
       "  ('seized', 'VBN'),\n",
       "  ('materials', 'NNS'),\n",
       "  ('and', 'CC'),\n",
       "  ('featured', 'VBD'),\n",
       "  ('sparring', 'VBG'),\n",
       "  ('between', 'IN'),\n",
       "  ('Cohen', 'NNP'),\n",
       "  ('’', 'NNP'),\n",
       "  ('s', 'NN'),\n",
       "  ('attorneys', 'NNS'),\n",
       "  ('and', 'CC'),\n",
       "  ('Michael', 'NNP'),\n",
       "  ('Avenatti', 'NNP'),\n",
       "  (',', ','),\n",
       "  ('an', 'DT'),\n",
       "  ('attorney', 'NN'),\n",
       "  ('for', 'IN'),\n",
       "  ('Stormy', 'NNP'),\n",
       "  ('Daniels', 'NNP'),\n",
       "  (',', ','),\n",
       "  ('an', 'DT'),\n",
       "  ('adult-film', 'JJ'),\n",
       "  ('actress', 'NN'),\n",
       "  ('to', 'TO'),\n",
       "  ('whom', 'WP'),\n",
       "  ('Cohen', 'NNP'),\n",
       "  ('paid', 'VBD'),\n",
       "  ('$', '$'),\n",
       "  ('130,000', 'CD'),\n",
       "  ('after', 'IN'),\n",
       "  ('she', 'PRP'),\n",
       "  ('alleged', 'VBD'),\n",
       "  ('she', 'PRP'),\n",
       "  ('had', 'VBD'),\n",
       "  ('an', 'DT'),\n",
       "  ('affair', 'NN'),\n",
       "  ('with', 'IN'),\n",
       "  ('Trump', 'NNP'),\n",
       "  ('.', '.')],\n",
       " [('Avenatti', 'NNP'),\n",
       "  ('had', 'VBD'),\n",
       "  ('sought', 'VBN'),\n",
       "  ('to', 'TO'),\n",
       "  ('join', 'VB'),\n",
       "  ('the', 'DT'),\n",
       "  ('case', 'NN'),\n",
       "  ('but', 'CC'),\n",
       "  ('withdrew', 'VBD'),\n",
       "  ('his', 'PRP$'),\n",
       "  ('motion', 'NN'),\n",
       "  ('after', 'IN'),\n",
       "  ('U.S.', 'NNP'),\n",
       "  ('District', 'NNP'),\n",
       "  ('Judge', 'NNP'),\n",
       "  ('Kimba', 'NNP'),\n",
       "  ('Wood', 'NNP'),\n",
       "  ('warned', 'VBD'),\n",
       "  ('that', 'IN'),\n",
       "  ('Avenatti', 'NNP'),\n",
       "  ('would', 'MD'),\n",
       "  ('have', 'VB'),\n",
       "  ('to', 'TO'),\n",
       "  ('end', 'VB'),\n",
       "  ('his', 'PRP$'),\n",
       "  ('“', 'JJ'),\n",
       "  ('publicity', 'NN'),\n",
       "  ('tour', 'VBP'),\n",
       "  ('”', 'NN'),\n",
       "  ('if', 'IN'),\n",
       "  ('she', 'PRP'),\n",
       "  ('granted', 'VBD'),\n",
       "  ('him', 'PRP'),\n",
       "  ('standing', 'VBG'),\n",
       "  ('.', '.')],\n",
       " [('Avenatti', 'NNP'),\n",
       "  ('did', 'VBD'),\n",
       "  ('not', 'RB'),\n",
       "  ('elaborate', 'VB'),\n",
       "  ('on', 'IN'),\n",
       "  ('his', 'PRP$'),\n",
       "  ('decision', 'NN'),\n",
       "  ('in', 'IN'),\n",
       "  ('his', 'PRP$'),\n",
       "  ('motion', 'NN'),\n",
       "  ('to', 'TO'),\n",
       "  ('withdraw', 'VB'),\n",
       "  (',', ','),\n",
       "  ('noting', 'VBG'),\n",
       "  ('that', 'IN'),\n",
       "  ('he', 'PRP'),\n",
       "  ('could', 'MD'),\n",
       "  ('refile', 'VB'),\n",
       "  ('it', 'PRP'),\n",
       "  ('“', 'VB'),\n",
       "  ('if', 'IN'),\n",
       "  ('necessary', 'JJ'),\n",
       "  (',', ','),\n",
       "  ('at', 'IN'),\n",
       "  ('a', 'DT'),\n",
       "  ('later', 'JJ'),\n",
       "  ('time', 'NN'),\n",
       "  (',', ','),\n",
       "  ('”', 'FW'),\n",
       "  ('depending', 'VBG'),\n",
       "  ('on', 'IN'),\n",
       "  ('what', 'WP'),\n",
       "  ('happens', 'VBZ'),\n",
       "  ('with', 'IN'),\n",
       "  ('Daniels', 'NNP'),\n",
       "  ('’', 'NNP'),\n",
       "  ('s', 'VBZ'),\n",
       "  ('motion', 'NN'),\n",
       "  ('to', 'TO'),\n",
       "  ('intervene', 'VB'),\n",
       "  ('in', 'IN'),\n",
       "  ('the', 'DT'),\n",
       "  ('case', 'NN'),\n",
       "  ('.', '.')],\n",
       " [('That', 'DT'),\n",
       "  ('motion', 'NN'),\n",
       "  ('has', 'VBZ'),\n",
       "  ('been', 'VBN'),\n",
       "  ('held', 'VBN'),\n",
       "  ('pending', 'VBG'),\n",
       "  ('discussions', 'NNS'),\n",
       "  ('between', 'IN'),\n",
       "  ('Avenatti', 'NNP'),\n",
       "  ('and', 'CC'),\n",
       "  ('federal', 'JJ'),\n",
       "  ('officials', 'NNS'),\n",
       "  (',', ','),\n",
       "  ('according', 'VBG'),\n",
       "  ('to', 'TO'),\n",
       "  ('court', 'NN'),\n",
       "  ('records', 'NNS'),\n",
       "  ('.', '.')],\n",
       " [('Avenatti', 'RB'),\n",
       "  ('pointed', 'VBN'),\n",
       "  ('to', 'TO'),\n",
       "  ('that', 'DT'),\n",
       "  ('motion', 'NN'),\n",
       "  (',', ','),\n",
       "  ('rather', 'RB'),\n",
       "  ('than', 'IN'),\n",
       "  ('the', 'DT'),\n",
       "  ('judge', 'NN'),\n",
       "  ('’', 'NNP'),\n",
       "  ('s', 'VBD'),\n",
       "  ('skepticism', 'NN'),\n",
       "  ('Wednesday', 'NNP'),\n",
       "  (',', ','),\n",
       "  ('in', 'IN'),\n",
       "  ('explaining', 'VBG'),\n",
       "  ('why', 'WRB'),\n",
       "  ('he', 'PRP'),\n",
       "  ('suddenly', 'RB'),\n",
       "  ('withdrew', 'VBD'),\n",
       "  ('.', '.')],\n",
       " [('“', 'NN'),\n",
       "  ('It', 'PRP'),\n",
       "  ('is', 'VBZ'),\n",
       "  ('no', 'RB'),\n",
       "  ('longer', 'RB'),\n",
       "  ('necessary', 'JJ'),\n",
       "  ('in', 'IN'),\n",
       "  ('light', 'NN'),\n",
       "  ('of', 'IN'),\n",
       "  ('the', 'DT'),\n",
       "  ('motion', 'NN'),\n",
       "  ('to', 'TO'),\n",
       "  ('intervene', 'VB'),\n",
       "  (',', ','),\n",
       "  ('which', 'WDT'),\n",
       "  ('may', 'MD'),\n",
       "  ('not', 'RB'),\n",
       "  ('be', 'VB'),\n",
       "  ('necessary', 'JJ'),\n",
       "  (',', ','),\n",
       "  ('being', 'VBG'),\n",
       "  ('delayed', 'VBN'),\n",
       "  (',', ','),\n",
       "  ('”', 'JJ'),\n",
       "  ('Avenatti', 'NNP'),\n",
       "  ('wrote', 'VBD'),\n",
       "  ('in', 'IN'),\n",
       "  ('an', 'DT'),\n",
       "  ('email', 'NN'),\n",
       "  ('.', '.')],\n",
       " [('“', 'NN'),\n",
       "  ('We', 'PRP'),\n",
       "  ('will', 'MD'),\n",
       "  ('refile', 'VB'),\n",
       "  ('it', 'PRP'),\n",
       "  ('when', 'WRB'),\n",
       "  ('and', 'CC'),\n",
       "  ('if', 'IN'),\n",
       "  ('the', 'DT'),\n",
       "  ('motion', 'NN'),\n",
       "  ('is', 'VBZ'),\n",
       "  ('taken', 'VBN'),\n",
       "  ('up.', 'JJ'),\n",
       "  ('”', 'NNP'),\n",
       "  ('Michael', 'NNP'),\n",
       "  ('Cohen', 'NNP'),\n",
       "  ('in', 'IN'),\n",
       "  ('New', 'NNP'),\n",
       "  ('York', 'NNP'),\n",
       "  ('earlier', 'RB'),\n",
       "  ('this', 'DT'),\n",
       "  ('month', 'NN'),\n",
       "  ('.', '.')],\n",
       " [('Prosecutors', 'NNS'),\n",
       "  ('already', 'RB'),\n",
       "  ('have', 'VBP'),\n",
       "  ('access', 'NN'),\n",
       "  ('to', 'TO'),\n",
       "  ('nearly', 'RB'),\n",
       "  ('300,000', 'CD'),\n",
       "  ('potential', 'JJ'),\n",
       "  ('pieces', 'NNS'),\n",
       "  ('of', 'IN'),\n",
       "  ('evidence', 'NN'),\n",
       "  ('seized', 'VBN'),\n",
       "  ('in', 'IN'),\n",
       "  ('an', 'DT'),\n",
       "  ('April', 'NNP'),\n",
       "  ('raid', 'NN'),\n",
       "  ('of', 'IN'),\n",
       "  ('Cohen', 'NNP'),\n",
       "  ('’', 'NNP'),\n",
       "  ('s', 'JJ'),\n",
       "  ('office', 'NN'),\n",
       "  ('and', 'CC'),\n",
       "  ('residences', 'NNS'),\n",
       "  ('.', '.')],\n",
       " [('(', '('),\n",
       "  ('Drew', 'NNP'),\n",
       "  ('Angerer/Getty', 'NNP'),\n",
       "  ('Images', 'NNP'),\n",
       "  (')', ')'),\n",
       "  ('[', 'VBD'),\n",
       "  ('Michael', 'NNP'),\n",
       "  ('Avenatti', 'NNP'),\n",
       "  ('is', 'VBZ'),\n",
       "  ('using', 'VBG'),\n",
       "  ('Trump', 'NNP'),\n",
       "  ('’', 'NNP'),\n",
       "  ('s', 'NN'),\n",
       "  ('tactics', 'NNS'),\n",
       "  ('to', 'TO'),\n",
       "  ('battle', 'NN'),\n",
       "  ('Trump', 'NNP'),\n",
       "  (']', 'NNP'),\n",
       "  ('Cohen', 'NNP'),\n",
       "  (',', ','),\n",
       "  ('who', 'WP'),\n",
       "  ('attended', 'VBD'),\n",
       "  ('the', 'DT'),\n",
       "  ('hearing', 'NN'),\n",
       "  ('but', 'CC'),\n",
       "  ('did', 'VBD'),\n",
       "  ('not', 'RB'),\n",
       "  ('speak', 'VB'),\n",
       "  (',', ','),\n",
       "  ('is', 'VBZ'),\n",
       "  ('being', 'VBG'),\n",
       "  ('investigated', 'VBN'),\n",
       "  ('for', 'IN'),\n",
       "  ('possible', 'JJ'),\n",
       "  ('bank', 'NN'),\n",
       "  ('and', 'CC'),\n",
       "  ('wire', 'NN'),\n",
       "  ('fraud', 'NN'),\n",
       "  ('and', 'CC'),\n",
       "  ('campaign', 'NN'),\n",
       "  ('finance', 'NN'),\n",
       "  ('violations', 'NNS'),\n",
       "  ('as', 'IN'),\n",
       "  ('part', 'NN'),\n",
       "  ('of', 'IN'),\n",
       "  ('a', 'DT'),\n",
       "  ('probe', 'NN'),\n",
       "  ('looking', 'VBG'),\n",
       "  ('at', 'IN'),\n",
       "  ('his', 'PRP$'),\n",
       "  ('efforts', 'NNS'),\n",
       "  ('to', 'TO'),\n",
       "  ('suppress', 'VB'),\n",
       "  ('damaging', 'JJ'),\n",
       "  ('stories', 'NNS'),\n",
       "  ('about', 'IN'),\n",
       "  ('Trump', 'NNP'),\n",
       "  ('—', 'NNP'),\n",
       "  ('such', 'JJ'),\n",
       "  ('as', 'IN'),\n",
       "  ('Daniels', 'NNP'),\n",
       "  ('’', 'NNP'),\n",
       "  ('s', 'VBD'),\n",
       "  ('claims', 'NNS'),\n",
       "  ('of', 'IN'),\n",
       "  ('a', 'DT'),\n",
       "  ('sexual', 'JJ'),\n",
       "  ('encounter', 'NN'),\n",
       "  ('with', 'IN'),\n",
       "  ('him', 'PRP'),\n",
       "  ('years', 'NNS'),\n",
       "  ('earlier', 'RBR'),\n",
       "  ('—', 'RB'),\n",
       "  ('just', 'RB'),\n",
       "  ('before', 'IN'),\n",
       "  ('the', 'DT'),\n",
       "  ('2016', 'CD'),\n",
       "  ('presidential', 'JJ'),\n",
       "  ('election', 'NN'),\n",
       "  ('.', '.')],\n",
       " [('As', 'IN'),\n",
       "  ('part', 'NN'),\n",
       "  ('of', 'IN'),\n",
       "  ('the', 'DT'),\n",
       "  ('probe', 'NN'),\n",
       "  (',', ','),\n",
       "  ('federal', 'JJ'),\n",
       "  ('agents', 'NNS'),\n",
       "  ('seized', 'VBD'),\n",
       "  ('Cohen', 'NNP'),\n",
       "  ('’', 'NNP'),\n",
       "  ('s', 'NN'),\n",
       "  ('records', 'NNS'),\n",
       "  ('and', 'CC'),\n",
       "  ('documents', 'NNS'),\n",
       "  ('on', 'IN'),\n",
       "  ('April', 'NNP'),\n",
       "  ('9', 'CD'),\n",
       "  (',', ','),\n",
       "  ('prompting', 'VBG'),\n",
       "  ('a', 'DT'),\n",
       "  ('legal', 'JJ'),\n",
       "  ('fight', 'NN'),\n",
       "  ('over', 'IN'),\n",
       "  ('who', 'WP'),\n",
       "  ('would', 'MD'),\n",
       "  ('get', 'VB'),\n",
       "  ('access', 'NN'),\n",
       "  ('to', 'TO'),\n",
       "  ('the', 'DT'),\n",
       "  ('materials', 'NNS'),\n",
       "  ('and', 'CC'),\n",
       "  ('when', 'WRB'),\n",
       "  ('.', '.')],\n",
       " [('Cohen', 'NNP'),\n",
       "  ('and', 'CC'),\n",
       "  ('Trump', 'NNP'),\n",
       "  ('both', 'DT'),\n",
       "  ('argued', 'VBD'),\n",
       "  ('that', 'IN'),\n",
       "  ('their', 'PRP$'),\n",
       "  ('attorneys', 'NNS'),\n",
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       "  ('the', 'DT'),\n",
       "  ('case', 'NN'),\n",
       "  (',', ','),\n",
       "  ('providing', 'VBG'),\n",
       "  ('the', 'DT'),\n",
       "  ('hearing', 'NN'),\n",
       "  ('’', 'NNP'),\n",
       "  ('s', 'NN'),\n",
       "  ('main', 'JJ'),\n",
       "  ('drama', 'NN'),\n",
       "  ('.', '.')],\n",
       " [('Cohen', 'NNP'),\n",
       "  ('’', 'NNP'),\n",
       "  ('s', 'NN'),\n",
       "  ('attorneys', 'NNS'),\n",
       "  ('and', 'CC'),\n",
       "  ('Avenatti', 'NNP'),\n",
       "  ('have', 'VBP'),\n",
       "  ('engaged', 'VBN'),\n",
       "  ('in', 'IN'),\n",
       "  ('an', 'DT'),\n",
       "  ('extended', 'JJ'),\n",
       "  ('back-and-forth', 'NN'),\n",
       "  ('over', 'IN'),\n",
       "  ('the', 'DT'),\n",
       "  ('question', 'NN'),\n",
       "  (',', ','),\n",
       "  ('filing', 'VBG'),\n",
       "  ('dueling', 'VBG'),\n",
       "  ('letters', 'NNS'),\n",
       "  ('Tuesday', 'NNP'),\n",
       "  ('night', 'NN'),\n",
       "  ('and', 'CC'),\n",
       "  ('then', 'RB'),\n",
       "  ('arguing', 'VBG'),\n",
       "  ('in', 'IN'),\n",
       "  ('court', 'NN'),\n",
       "  ('Wednesday', 'NNP'),\n",
       "  ('.', '.')],\n",
       " [('[', 'NN'),\n",
       "  ('Michael', 'NNP'),\n",
       "  ('Cohen', 'NNP'),\n",
       "  ('’', 'NNP'),\n",
       "  ('s', 'NN'),\n",
       "  ('lawyers', 'NNS'),\n",
       "  ('accuse', 'VBP'),\n",
       "  ('Avenatti', 'NNP'),\n",
       "  ('of', 'IN'),\n",
       "  ('spreading', 'VBG'),\n",
       "  ('false', 'JJ'),\n",
       "  ('information', 'NN'),\n",
       "  (']', 'NN'),\n",
       "  ('When', 'WRB'),\n",
       "  ('he', 'PRP'),\n",
       "  ('entered', 'VBD'),\n",
       "  ('the', 'DT'),\n",
       "  ('courtroom', 'NN'),\n",
       "  ('Wednesday', 'NNP'),\n",
       "  (',', ','),\n",
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       "  ('approached', 'VBD'),\n",
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       "  ('bench', 'NN'),\n",
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       "  ('out', 'RP'),\n",
       "  ('where', 'WRB'),\n",
       "  ('he', 'PRP'),\n",
       "  ('should', 'MD'),\n",
       "  ('sit', 'VB'),\n",
       "  ('.', '.')],\n",
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       "  ('pointed', 'VBN'),\n",
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       "  ('Trump', 'NNP'),\n",
       "  ('Organization', 'NNP'),\n",
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       "  ('Keith', 'NNP'),\n",
       "  ('Davidson', 'NNP'),\n",
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       "  ('Daniels', 'NNP'),\n",
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       "  ('130,000', 'CD'),\n",
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       "  ('received', 'VBD'),\n",
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       "  ('“', 'JJ'),\n",
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       "  ('surprising', 'JJ'),\n",
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       "  ('Stephanie', 'NNP'),\n",
       "  ('Clifford', 'NNP'),\n",
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       " [('Stephen', 'NNP'),\n",
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       "  ('to', 'TO'),\n",
       "  ('participate', 'VB'),\n",
       "  ('in', 'IN'),\n",
       "  ('the', 'DT'),\n",
       "  ('process', 'NN'),\n",
       "  (',', ','),\n",
       "  ('he', 'PRP'),\n",
       "  ('would', 'MD'),\n",
       "  ('“', 'VB'),\n",
       "  ('not', 'RB'),\n",
       "  ('be', 'VB'),\n",
       "  ('permitted', 'VBN'),\n",
       "  ('to', 'TO'),\n",
       "  ('use', 'VB'),\n",
       "  ('this', 'DT'),\n",
       "  ('court', 'NN'),\n",
       "  ('as', 'IN'),\n",
       "  ('a', 'DT'),\n",
       "  ('platform', 'NN'),\n",
       "  ('for', 'IN'),\n",
       "  ('anything', 'NN'),\n",
       "  ('”', 'NN'),\n",
       "  ('and', 'CC'),\n",
       "  ('said', 'VBD'),\n",
       "  ('he', 'PRP'),\n",
       "  ('could', 'MD'),\n",
       "  ('no', 'RB'),\n",
       "  ('longer', 'RBR'),\n",
       "  ('declare', 'VB'),\n",
       "  ('that', 'DT'),\n",
       "  ('Cohen', 'NNP'),\n",
       "  ('is', 'VBZ'),\n",
       "  ('guilty', 'JJ'),\n",
       "  ('of', 'IN'),\n",
       "  ('potential', 'JJ'),\n",
       "  ('charges', 'NNS'),\n",
       "  ('.', '.')],\n",
       " [('If', 'IN'),\n",
       "  ('Avenatti', 'NNP'),\n",
       "  ('had', 'VBD'),\n",
       "  ('been', 'VBN'),\n",
       "  ('given', 'VBN'),\n",
       "  ('standing', 'VBG'),\n",
       "  ('in', 'IN'),\n",
       "  ('the', 'DT'),\n",
       "  ('case', 'NN'),\n",
       "  (',', ','),\n",
       "  ('Wood', 'NNP'),\n",
       "  ('said', 'VBD'),\n",
       "  (',', ','),\n",
       "  ('he', 'PRP'),\n",
       "  ('would', 'MD'),\n",
       "  ('have', 'VB'),\n",
       "  ('to', 'TO'),\n",
       "  ('“', 'VB'),\n",
       "  ('stop', 'VB'),\n",
       "  ('in', 'IN'),\n",
       "  ('its', 'PRP$'),\n",
       "  ('tracks', 'NNS'),\n",
       "  ('your', 'PRP$'),\n",
       "  ('publicity', 'NN'),\n",
       "  ('tour', 'NN'),\n",
       "  ('on', 'IN'),\n",
       "  ('television', 'NN'),\n",
       "  ('and', 'CC'),\n",
       "  ('elsewhere.', 'NN'),\n",
       "  ('”', 'NN'),\n",
       "  ('After', 'IN'),\n",
       "  ('leaving', 'VBG'),\n",
       "  ('the', 'DT'),\n",
       "  ('hearing', 'NN'),\n",
       "  (',', ','),\n",
       "  ('Avenatti', 'NNP'),\n",
       "  ('offered', 'VBD'),\n",
       "  ('an', 'DT'),\n",
       "  ('energetic', 'JJ'),\n",
       "  ('condemnation', 'NN'),\n",
       "  ('of', 'IN'),\n",
       "  ('Cohen', 'NNP'),\n",
       "  ('to', 'TO'),\n",
       "  ('the', 'DT'),\n",
       "  ('press', 'NN'),\n",
       "  ('assembled', 'VBD'),\n",
       "  ('outside', 'IN'),\n",
       "  ('the', 'DT'),\n",
       "  ('courthouse', 'NN'),\n",
       "  ('.', '.')],\n",
       " [('He', 'PRP'),\n",
       "  ('withdrew', 'VBD'),\n",
       "  ('his', 'PRP$'),\n",
       "  ('motion', 'NN'),\n",
       "  ('to', 'TO'),\n",
       "  ('join', 'VB'),\n",
       "  ('a', 'DT'),\n",
       "  ('short', 'JJ'),\n",
       "  ('time', 'NN'),\n",
       "  ('later', 'RBR'),\n",
       "  ('.', '.')],\n",
       " [('Berman', 'NNP'),\n",
       "  ('reported', 'VBD'),\n",
       "  ('from', 'IN'),\n",
       "  ('Washington', 'NNP'),\n",
       "  ('.', '.')]]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def load_file(path):\n",
    "    # Relative lookup from the data directory \n",
    "    path = os.path.join(DATA, path)\n",
    "    \n",
    "    with open(path, 'rb') as f:\n",
    "        return pickle.load(f)\n",
    "\n",
    "    \n",
    "load_file(\"politics/2H0Yc36.pickle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Corpus Analytics  \n",
    "\n",
    "Once we have constructed a corpus, we can create a corpus reader object to begin some high level descriptive analyses of the whole corpus. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Corpus Reader \n",
    "\n",
    "A corpus reader object gives you _steaming_ access to your corpus in a memory-safe fashion. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from nltk.corpus.reader.api import CorpusReader\n",
    "from nltk.corpus.reader.api import CategorizedCorpusReader\n",
    "\n",
    "PKL_PATTERN = r'(?!\\.)[a-z_\\s]+[/\\\\][a-f0-9]+\\.pickle'\n",
    "CAT_PATTERN = r'([a-z_\\s]+)[/\\\\].*'\n",
    "\n",
    "\n",
    "class PickledCorpus(CategorizedCorpusReader, CorpusReader):\n",
    "    \n",
    "    def __init__(self, root, fileids=PKL_PATTERN, cat_pattern=CAT_PATTERN):\n",
    "        CategorizedCorpusReader.__init__(self, {\"cat_pattern\": cat_pattern})\n",
    "        CorpusReader.__init__(self, root, fileids)\n",
    "        \n",
    "    def docs(self):\n",
    "        fileids = self.fileids()\n",
    "        \n",
    "        # Create a generator, loading one document into memory at a time.\n",
    "        for path, enc, fileid in self.abspaths(fileids, True, True):\n",
    "            with open(path, 'rb') as f:\n",
    "                yield pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2538 documents in 12 categories:\n",
      "\n",
      "books\n",
      "business\n",
      "cinema\n",
      "cooking\n",
      "data_science\n",
      "design\n",
      "do_it_yourself\n",
      "gaming\n",
      "news\n",
      "politics\n",
      "sports\n",
      "tech\n"
     ]
    }
   ],
   "source": [
    "# Using the fully implemented corpus reader \n",
    "from utils.reader import PickledCorpusReader  \n",
    "\n",
    "corpus = PickledCorpusReader(\"data/corpus\")\n",
    "print(\"{} documents in {} categories:\\n\".format(len(corpus.fileids()), len(corpus.categories())))\n",
    "print(\"\\n\".join(corpus.categories()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Corpus Metrics \n",
    "\n",
    "- **Size**: total number of tokens in the corpus\n",
    "- **Vocabulary**: total number of unique tokens in the corpus \n",
    "- **Lexical Diversity**: the ratio of size to vocabulary, roughly how many times each token is used. \n",
    "\n",
    "These can be extended to per-document or per-category metrics as well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category          Vocabulary    Word Count\n",
      "--------------  ------------  ------------\n",
      "books                   7795         41438\n",
      "business               18982        222182\n",
      "cinema                 10274         69153\n",
      "cooking                 5038         37854\n",
      "data_science            5759         31354\n",
      "design                  4313         18260\n",
      "do_it_yourself          5180         28050\n",
      "gaming                  9120         70778\n",
      "news                   39801        850688\n",
      "politics                9951         88853\n",
      "sports                  8028         68884\n",
      "tech                   12410         97368\n"
     ]
    }
   ],
   "source": [
    "from nltk import ConditionalFreqDist\n",
    "\n",
    "# Conditional frequency on category \n",
    "counts = ConditionalFreqDist()\n",
    "\n",
    "# Count words and vocabulary by category \n",
    "for category in corpus.categories():\n",
    "    for word in corpus.words(categories=[category]):\n",
    "        counts[category][word] += 1 \n",
    "\n",
    "# Print a table \n",
    "table = [[\"Category\", \"Vocabulary\", \"Word Count\"]]\n",
    "        \n",
    "for category, counts in counts.items():\n",
    "    table.append([\n",
    "        category, len(counts), sum(counts.values())\n",
    "    ])\n",
    "\n",
    "print(tabulate(table, headers='firstrow'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Token Frequency \n",
    "\n",
    "- Token frequency and context is the basis of all text analytics. \n",
    "- We expect a Zipfian distribution for each token. \n",
    "- However, from a pure data perspective there is a lot of structural \"noise\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from yellowbrick.text import FreqDistVisualizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "vectorizer = CountVectorizer()\n",
    "docs       = vectorizer.fit_transform(list(corpus.raw_data()))\n",
    "visualizer = FreqDistVisualizer(features=vectorizer.get_feature_names())\n",
    "\n",
    "visualizer.fit(docs)\n",
    "visualizer.poof()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vectorizer = CountVectorizer(stop_words='english')\n",
    "docs       = vectorizer.fit_transform(list(corpus.raw_data()))\n",
    "visualizer = FreqDistVisualizer(\n",
    "    features=vectorizer.get_feature_names(), \n",
    "    title=\"Frequency Distribution of Top 50 Tokens, Stopwords Removed\"\n",
    ")\n",
    "\n",
    "visualizer.fit(docs)\n",
    "visualizer.poof()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vectorizer = CountVectorizer(stop_words='english')\n",
    "docs       = vectorizer.fit_transform(\n",
    "    list(corpus.raw_data(categories=[\"business\", \"technology\"]))\n",
    ")\n",
    "visualizer = FreqDistVisualizer(\n",
    "    features=vectorizer.get_feature_names(), \n",
    "    title=\"Frequency Distribution of Business/Tech Categories\"\n",
    ")\n",
    "\n",
    "visualizer.fit(docs)\n",
    "visualizer.poof()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Document Similarity \n",
    "\n",
    "Although we'll discuss vectorization in more detail during the machine learning part of the course, we can use document vectors to show groups of related documents by their contents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from yellowbrick.text import TSNEVisualizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "cats   = [\"business\", \"design\", \"news\", \"politics\", \"sports\", \"tech\"]\n",
    "tfidf  = TfidfVectorizer()\n",
    "docs   = tfidf.fit_transform(list(corpus.raw_data(categories=cats)))\n",
    "\n",
    "tsne = TSNEVisualizer()\n",
    "tsne.fit(docs, list(corpus.labels(categories=cats)))\n",
    "tsne.poof()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Word Forms and Search \n",
    "\n",
    "One of the most common forms of text analytics is search -- finding documents that contain specific concepts. However, a string matching search on even our preprocessed data will require additional normalization for case and multiple word forms. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Stemming \n",
    "\n",
    "The simplest (and most commonly used) word form normalization is _stemming_; simply chop off affixes to create a root stem whose representation is the word form. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'gener'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.stem.porter import PorterStemmer\n",
    "\n",
    "stemmer = PorterStemmer()\n",
    "stemmer.stem(\"generously\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arabic danish dutch english finnish french german hungarian italian norwegian porter portuguese romanian russian spanish swedish\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'generous'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.stem.snowball import SnowballStemmer\n",
    "\n",
    "# Supported languages \n",
    "print(\" \".join(SnowballStemmer.languages))\n",
    "\n",
    "# Create an english stemmer \n",
    "stemmer = SnowballStemmer(\"english\")\n",
    "stemmer.stem(\"generously\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Lemmatization \n",
    "\n",
    "Rather than perform string manipulation, simply look up the root word by part of speech tag in a lexicon. This is far more accurate but incurs a performance penalty relative to the size of the corpus. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'wait'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.corpus import wordnet as wn\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "\n",
    "\n",
    "class Lemmatizer(object):\n",
    "    \n",
    "    tag_map = {\n",
    "        'N': wn.NOUN,\n",
    "        'V': wn.VERB,\n",
    "        'R': wn.ADV,\n",
    "        'J': wn.ADJ\n",
    "    }\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.lemmatizer = WordNetLemmatizer()\n",
    "    \n",
    "    def __call__(self, token, pos_tag):\n",
    "        return self.lemmatize(token, pos_tag)\n",
    "\n",
    "    def lemmatize(self, token, pos_tag):\n",
    "        tag = self.tag_map.get(pos_tag[0], wn.NOUN)\n",
    "        return self.lemmatizer.lemmatize(token, tag)\n",
    "\n",
    "\n",
    "# Create the callable lemmatizer\n",
    "lemmatize = Lemmatizer() \n",
    "lemmatize(\"waiting\", \"VB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "she sold seashel by the seashor on the 4th of juli .\n",
      "She sell seashell by the seashore on the 4th of July .\n"
     ]
    }
   ],
   "source": [
    "sentence = list(word_tokenize(\"She sold seashells by the seashore on the 4th of July.\"))\n",
    "\n",
    "print(\" \".join([stemmer.stem(token) for token in sentence]))\n",
    "print(\" \".join([lemmatize(token, tag) for token, tag in pos_tag(sentence)]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Search \n",
    "\n",
    "There are a lot of methods for search, but here we will look at a simple mechanism I use regularly in my research to quickly find context for a specific word or phrase: the `concordance()` method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from nltk.text import Text\n",
    "\n",
    "\n",
    "class Search(object):\n",
    "    \n",
    "    def __init__(self, corpus):\n",
    "        self.text = Text([\n",
    "            lemmatize(token.lower(), tag) \n",
    "            for token, tag in corpus.tagged()\n",
    "        ])\n",
    "    \n",
    "    def __call__(self, term, tag=\"NN\"):\n",
    "        return self.search(term, tag)\n",
    "    \n",
    "    def search(self, term, tag=\"NN\"):\n",
    "        term = lemmatize(term, tag)\n",
    "        return self.text.concordance(term)\n",
    "\n",
    "    \n",
    "search = Search(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying 25 of 25 matches:\n",
      "ch this video washington — federal regulator on thursday propose a set of priva\n",
      "now , japan ’ s financial industry regulator hop relaxed rule on invest in fina\n",
      "now , japan ’ s financial industry regulator hop relaxed rule on invest in fina\n",
      " supervision since june , when the regulator conduct an audit and instruct the \n",
      "ajor u . s . bank fail to persuade regulator they could go bankrupt without dis\n",
      " their so - call living will after regulator say the submission fail to satisfy\n",
      " their plan fell far short of what regulator deem acceptable . since then , the\n",
      "se over the course of year be that regulator could eventually get authority to \n",
      ", because this mark the first time regulator have take the initial step to find\n",
      "h bank receive a letter detail the regulator ’ expectation . the note to jpmorg\n",
      "o promote an easy resolution , the regulator say . the living - will setback fo\n",
      " the eight u . s . institution the regulator respond to will be give until that\n",
      "ccord to the country ’ s insurance regulator . siam commercial have more than 1\n",
      "uld satisfy federal and california regulator . the propose deal be expect to le\n",
      "ve to already have the blessing of regulator at the u . s . environmental prote\n",
      "t the time , india ’ s food safety regulator deem the product “ unsafe and haza\n",
      "f india , the nation ’ s financial regulator , be push lender to ensure they ha\n",
      "ider , we be completely align with regulator in their effort to ensure that con\n",
      " . we consult and collaborate with regulator and work hard to comply with law a\n",
      "et stiff antitrust resistance from regulator in the u . s . and europe , a pers\n",
      "douglas elliman real estatefederal regulator may be crack down on wall street b\n",
      "of 571 million pound , the pension regulator be probe whether bh ' s previous o\n",
      "minor henchman in a group call the regulator . she may be spin a character name\n",
      "gulation . instead , communication regulator ofcom should be responsible for al\n",
      "st for it role a “ cheerleader and regulator ” lead to confusion about where it\n"
     ]
    }
   ],
   "source": [
    "search(\"regulator\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## N-Gram Analysis \n",
    "\n",
    "Now that we've broken apart our text into our smallest unit of work (tokens), we can start to build it back up into a computable form. The first, and simplest, method creates windows of context by aligning co-occuring words."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "An N-Gram is a static group of N tokens, generated by sliding a fixed length window along the text, usually respecting sentence boundaries. For example, here is the generation of trigrams (N=3) on a sentence:\n",
    "\n",
    "![N-Grams](figures/s1_ngrams.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from nltk import ngrams as nltk_ngrams\n",
    "from functools import partial\n",
    "\n",
    "LPAD_SYMBOL = \"<s>\"\n",
    "RPAD_SYMBOL = \"</s>\"\n",
    "\n",
    "nltk_ngrams = partial(nltk_ngrams,\n",
    "    pad_right=True, pad_left=True,\n",
    "    right_pad_symbol=RPAD_SYMBOL, \n",
    "    left_pad_symbol=LPAD_SYMBOL\n",
    ")\n",
    "\n",
    "\n",
    "def ngrams(text, n=3):\n",
    "    for sent in sent_tokenize(text):\n",
    "        sent = word_tokenize(sent)\n",
    "        for ngram in nltk_ngrams(sent, n):\n",
    "            yield ngram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('<s>', '<s>', 'The')\n",
      "('<s>', 'The', 'Capitals')\n",
      "('The', 'Capitals', 'persevered')\n",
      "('Capitals', 'persevered', 'in')\n",
      "('persevered', 'in', 'large')\n",
      "('in', 'large', 'part')\n",
      "('large', 'part', 'because')\n",
      "('part', 'because', 'of')\n",
      "('because', 'of', 'Eller')\n",
      "('of', 'Eller', '’')\n",
      "('Eller', '’', 's')\n",
      "('’', 's', 'effort')\n",
      "('s', 'effort', 'against')\n",
      "('effort', 'against', 'the')\n",
      "('against', 'the', 'Golden')\n",
      "('the', 'Golden', 'Knights')\n",
      "('Golden', 'Knights', ',')\n",
      "('Knights', ',', 'tying')\n",
      "(',', 'tying', 'the')\n",
      "('tying', 'the', 'series')\n",
      "('the', 'series', 'at')\n",
      "('series', 'at', 'a')\n",
      "('at', 'a', 'game')\n",
      "('a', 'game', 'apiece')\n",
      "('game', 'apiece', 'with')\n",
      "('apiece', 'with', 'the')\n",
      "('with', 'the', 'next')\n",
      "('the', 'next', 'two')\n",
      "('next', 'two', 'games')\n",
      "('two', 'games', 'in')\n",
      "('games', 'in', 'Washington')\n",
      "('in', 'Washington', '.')\n",
      "('Washington', '.', '</s>')\n",
      "('.', '</s>', '</s>')\n",
      "('<s>', '<s>', 'This')\n",
      "('<s>', 'This', 'was')\n",
      "('This', 'was', 'the')\n",
      "('was', 'the', 'first')\n",
      "('the', 'first', 'Stanley')\n",
      "('first', 'Stanley', 'Cup')\n",
      "('Stanley', 'Cup', 'finals')\n",
      "('Cup', 'finals', 'game')\n",
      "('finals', 'game', 'win')\n",
      "('game', 'win', 'in')\n",
      "('win', 'in', 'franchise')\n",
      "('in', 'franchise', 'history')\n",
      "('franchise', 'history', '.')\n",
      "('history', '.', '</s>')\n",
      "('.', '</s>', '</s>')\n"
     ]
    }
   ],
   "source": [
    "text = \"\"\"\n",
    "The Capitals persevered in large part because of Eller’s effort against \n",
    "the Golden Knights, tying the series at a game apiece with the next \n",
    "two games in Washington. This was the first Stanley Cup finals game win\n",
    "in franchise history.\n",
    "\"\"\"\n",
    "\n",
    "for trigram in ngrams(text):\n",
    "    print(trigram)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Significant Collocations \n",
    "\n",
    "Obviously, some trigrams impart more information than others, e.g. `\"Stanley Cup finals\"` or `\"tying the series\"` vs. `\"persevered in large\"`. \n",
    "\n",
    "We can use association measures and statistical scoring methods to rank N-Grams by significance, further filtering and ranking the information retrieved from the corpus. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[((\"'\", 's', '.', 'The'), 58543.4237454517), (('.', 'It', \"'\", 's'), 50516.65684363094), ((\"'\", 's', 'in', 'the'), 49633.5229348087), (('New', 'York', \"'\", 's'), 48066.00150008884), ((\"'\", 's', 'New', 'York'), 48006.30814192213), ((\"'\", 's', ',', 'which'), 46942.84205595173), (('.', 'He', \"'\", 's'), 46816.14124398083), (('\"', 'It', \"'\", 's'), 46423.16312167403), (('It', \"'\", 's', 'not'), 46299.71632252438), (('“', 'It', \"'\", 's'), 46063.60968745475)]\n"
     ]
    }
   ],
   "source": [
    "from nltk.collocations import QuadgramCollocationFinder\n",
    "from nltk.metrics.association import QuadgramAssocMeasures\n",
    "\n",
    "\n",
    "# Extract quadgrams using the NLTK base collocation finder\n",
    "cats   = [\"business\", \"politics\", \"sports\", \"tech\"]\n",
    "ngrams = QuadgramCollocationFinder.from_words(corpus.words(categories=cats))\n",
    "\n",
    "# Rank collocations by an association metric\n",
    "scored = ngrams.score_ngrams(QuadgramAssocMeasures.likelihood_ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N-Gram          Score\n",
      "------------  -------\n",
      "' s . The     58543.4\n",
      ". It ' s      50516.7\n",
      "' s in the    49633.5\n",
      "New York ' s  48066\n",
      "' s New York  48006.3\n",
      "' s , which   46942.8\n",
      ". He ' s      46816.1\n",
      "\" It ' s      46423.2\n",
      "It ' s not    46299.7\n",
      "“ It ' s      46063.6\n"
     ]
    }
   ],
   "source": [
    "def print_scores(scored):\n",
    "    table = [[\"N-Gram\", \"Score\"]]\n",
    "    for ngram, score in scored:\n",
    "        table.append([\" \".join(ngram), score])\n",
    "    print(tabulate(table, headers=\"firstrow\"))\n",
    "    \n",
    "\n",
    "# Print top 10 \n",
    "print_scores(scored[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N-Gram                           Score\n",
      "-----------------------------  -------\n",
      "to have provided reasons       309.753\n",
      "do not audit safety            309.752\n",
      "with just the date             309.749\n",
      "guy they had rated             309.747\n",
      "the economy has improved       309.747\n",
      "tech companies have committed  309.744\n",
      ", Echo was prominently         309.741\n",
      "not yet begun to               309.738\n",
      "pick , guard was               309.737\n",
      "claiming their civil rights    309.736\n"
     ]
    }
   ],
   "source": [
    "# Print middle of scores \n",
    "print_scores(scored[len(scored)//2:len(scored)//2+10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "232964 filtered quadgrams from 439739\n",
      "\n",
      "N-Gram              Score\n",
      "----------------  -------\n",
      "New York ' s      48066\n",
      "“ It ' s          46063.6\n",
      "Donald Trump ' s  45538.8\n",
      ".\" It ' s         45244.6\n",
      ".” It ' s         45222.3\n",
      "“ it ' s          45200.8\n",
      "). It ' s         45165.5\n",
      "... It ' s        45106.4\n",
      ".? It ' s         45088\n",
      "2016 It ' s       45086.9\n",
      "AMC It ' s        45080.5\n",
      "navy It ' s       45080.5\n",
      "¦ It ' s                   45077.5\n",
      "Â It ' s          45076.7\n",
      "Images It ' s     45072.4\n",
      "ship It ' s       45071.9\n",
      "-- It ' s         45068.9\n",
      "think it ' s      45023.6\n",
      "said it ' s       44930.2\n",
      "whether it ' s    44929.4\n"
     ]
    }
   ],
   "source": [
    "import nltk \n",
    "import string \n",
    "\n",
    "stopwords = frozenset(nltk.corpus.stopwords.words('english') + list(string.punctuation))\n",
    "\n",
    "filtered_scored = [\n",
    "    (ngram, score) for ngram, score in scored \n",
    "    if ngram[0].lower() not in stopwords\n",
    "]\n",
    "\n",
    "print(\"{} filtered quadgrams from {}\\n\".format(len(filtered_scored), len(scored)))\n",
    "print_scores(filtered_scored[:20])"
   ]
  }
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