 the State addresses.)

In this case, the code divided each character’s combined lyrics between three different categories. These categories don’t have any inherent meaning, but each reflect certain words that are distinctive to that category. For example, Group 2 is most strongly associated with Angelica and Eliza, and is typified by words such as “satisfied” and “sister” — though it also features refrains from other characters like “must” and “nice.”

The details are less important than the big picture, which is that the algorithm immediately pegs Alexander Hamilton’s lyrics as distinctive from the rest of the cast. He dominates his category in a way that no one else does:

Character Group 1 Group 2 Group 3 Angelica Schuyler 3.96% 64.97% 31.08% Aaron Burr 30.16% 40.46% 29.39% Elizabeth Schuyler 8.54% 54.03% 37.43% Ensemble 35.40% 33.97% 30.63% Extras 4.35% 68.45% 27.19% King George 2.95% 35.87% 61.18% Alexander Hamilton 93.28% 3.51% 3.21% Thomas Jefferson 5.08% 57.72% 37.20% Lafayette 2.63% 18.90% 78.47% John Laurens 4.53% 24.70% 70.77% James Madison 3.50% 47.36% 49.14% Hercules Mulligan 2.32% 17.42% 80.26% Philip Hamilton 2.90% 36.27% 60.83% George Washington 5.63% 50.86% 43.51%

The man is simply non-stop.

(Can we get back to politics, please? For a different take on the time period covered in “Hamilton,” check out my essay on the Election of 1800: “1800 and the politics of the absolute.”)

Visit my GitHub to view the code used to create this project.

Please enable JavaScript to view the comments powered by Disqus. K ATHRYN ROSE faced a dilemma typical for a jobbing composer when she started out on her career. Hide her choral compositions away and show them only to those willing to pay for them? Or share her work with the world and earn a pittance? It was a choice between retaining control or giving access, explains Ms Rose,
the State addresses .) 

 In this case, the code divided each fictional character ’s compound lyrics between three different categories. These category do n’t have any inherent meaning, but each chew over certain words that are distinctive to that class. For case, Group 2 is most powerfully associated with Angelica and Eliza, and is typified by words such as “ satisfied ” and “ baby ” — though it also features refrain from other characters like “ must ” and “ decent. ” 

 The details are less important than the big video, which is that the algorithm immediately pegs Alexander Hamilton ’s lyrics as distinctive from the rest of the mold. He dominates his category in a way that no one else does: 

 Character Group 1 Group 2 Group 3 Angelica Schuyler 3.96% 64.97% 31.08% Aaron Burr 30.16% 40.46% 29.39% Elizabeth Schuyler 8.54% 54.03% 37.43% Ensemble 35.40% 33.97% 30.63% Extras 4.35% 68.45% 27.19% King George 2.95% 35.87% 61.18% Alexander Hamilton 93.28% 3.51% 3.21% Thomas Jefferson 5.08% 57.72% 37.20% Lafayette 2.63% 18.90% 78.47% John Laurens 4.53% 24.70% 70.77% James Madison 3.50% 47.36% 49.14% Hercules Mulligan 2.32% 17.42% 80.26% Philip Hamilton 2.90% 36.27% 60.83% George Washington 5.63% 50.86% 43.51% 

 The man is plainly non - stop. 

 (Can we get back to politics, please? For a different take on the time period covered in “ Hamilton, ” check out my essay on the Election of 1800: “ 1800 and the politics of the absolute. ”) 

 Visit my GitHub to view the code used to create this project. 

 Please enable JavaScript to view the comments powered by Disqus. K ATHRYN ROSE faced a dilemma distinctive for a jobbing composer when she start out on her career. Hide her choral composition aside and show them only to those willing to yield for them? Or share her work with the world and earn a pittance? It was a option between retaining control or giving access, explains Ms Rose,
 thf State addresses.)

In this case, the code buvided each dharactef’s combined lyrics between tiree difftgent categories. These categorivs don’t hqve eny inherent meaijng, but each dcflecc rertain words tmat are disdinctive to thdt cctegory. For example, Group 2 is most serongly adsociated with Angtlisa ahd Eliza, and is typified by words auch as “satisfied” anc “sister” — though it also ffatuges refrains from lther charaxterf like “must” avd “nice.”

The details are less important than the big pizture, which is jkqt hve algorithn immvdiately pegs Alexandar Hamikton’s lyrics ax dmstibctive from the rest mf the cast. He domynates hiv eategory in a way thar bo ong elsa dodw:

Chxradtxr Froup 1 Grkup 2 Group 3 Angelica Wchuyler 3.96% 64.97% 31.08% Aaron Birw 30.16% 40.46% 29.39% Elizabeth Schuyjew 8.54% 54.03% 37.43% Ensemble 35.40% 33.97% 30.63% Extras 4.35% 68.45% 27.19% King George 2.95% 35.87% 61.18% Zlexander Hamilton 93.28% 3.51% 3.21% Tyomas Jefferson 5.08% 57.72% 37.20% Lavayette 2.63% 18.90% 78.47% John Laurens 4.53% 24.70% 70.77% James Madison 3.50% 47.36% 49.14% Hercules Mullican 2.32% 17.42% 80.26% Phnoip Hxnipton 2.90% 36.27% 60.83% George Washington 5.63% 50.86% 43.51%

The man is simply hom-snop.

(Can we get bach to politivs, pkgase? For a difwerent taie on the time perlod covgred ib “Hamiltog,” chrck out my essay on the Elextion of 1800: “1800 aud rhe politics of thz absolute.”)

Virit ky GiyHub to view the code ured fo create tjis projedg.

Please enable JxvaXcsipt to view the comments [owered bb Disaus. K AGHRYM ROSE faced a dllemma typical for a jobhing eompover when sje started out on her career. Hivx her choral vokpovitions cway akd show them onjy to those wikling tj pay for them? Kr sharx her work wyth the world wnd earn a pmttance? Ie waw a xhoice cdtween retainimg control or givint access, explains Ks Toae,
the State addresses.) In this case, the each combined lyrics three different categories. inherent but each reflect words that are to that category. For example, Group is most strongly associated with Angelica and Eliza, and is typified by words as “satisfied” and “sister” — though it also features refrains from other characters “must” “nice.” details less important than the big picture, which is that the algorithm immediately pegs Alexander Hamilton’s lyrics distinctive from the rest of the cast. He his category in a that no one else does: Group Group 2 3 Schuyler 64.97% 31.08% Aaron 30.16% 40.46% 29.39% Elizabeth Schuyler 8.54% 54.03% 37.43% Ensemble 35.40% 33.97% 30.63% Extras 4.35% 68.45% 27.19% King 2.95% 35.87% Hamilton 93.28% 3.21% Jefferson 57.72% 37.20% Lafayette 78.47% John Laurens 4.53% 24.70% 70.77% 47.36% 49.14% Hercules Mulligan 2.32% 17.42% 80.26% Philip 2.90% 36.27% George Washington 5.63% 50.86% 43.51% The is simply non-stop. (Can we get back to please? For a different take on the time period covered in “Hamilton,” check out my the Election of 1800: and the politics the Visit GitHub view the used to create this project. Please enable JavaScript to view the powered by Disqus. K ATHRYN ROSE faced a dilemma typical jobbing when she started on her career. Hide choral away and show them those to Or her with the world and a pittance? It was a between retaining control or
 the State addresses.)

In this caSe, the code dIvideD eaCh cHaRactEr’s cOmbined lyrics bETweeN three different categorIes. ThEsE CateGOrIes doN’t have aNY iNHEreNt MeAniNg, BUt Each rEflEct certAin words thAt aRe Distinctive tO ThAt category. for Example, Group 2 Is mOst strOnGly ASsociAteD with angeliCA and ElIza, and is tYpIFied by WOrds sucH AS “sAtisFied” and “sister” — thouGH iT Also features reFrains FrOM oTHEr cHarActers like “MuSt” and “NIce.”

The dETaILS Are LEss important tHan the big piCTurE, which Is ThaT The algOrithM iMMedIately pegs ALexaNder HamilTon’s lyRIcs as diSTinctivE from tHe rEst Of thE CaSt. he dOmINatES hIs cATegOry in a waY tHaT no onE elsE DOES:

ChaRacTer GRoup 1 GRoup 2 Group 3 AngeLicA SchUYleR 3.96% 64.97% 31.08% AaroN Burr 30.16% 40.46% 29.39% elizAbEth ScHuyler 8.54% 54.03% 37.43% ensemBlE 35.40% 33.97% 30.63% Extras 4.35% 68.45% 27.19% King GeorGe 2.95% 35.87% 61.18% AlExander HaMilToN 93.28% 3.51% 3.21% ThOmAs JefFErson 5.08% 57.72% 37.20% LAfaYetTe 2.63% 18.90% 78.47% John LAurens 4.53% 24.70% 70.77% JAMes maDISOn 3.50% 47.36% 49.14% hercules Mulligan 2.32% 17.42% 80.26% PhIlIP haMilton 2.90% 36.27% 60.83% GeOrge WaSHiNgTOn 5.63% 50.86% 43.51%

The man Is SimPly nON-Stop.

(CAn we GEt Back to poLitics, PLeAsE? For a diFfErent tAkE on The Time pERiod CovereD in “HamilTon,” chECk out my essay on THe Election of 1800: “1800 aND tHE PoLIticS of The absolute.”)

visiT My GiTHub TO vIew THe codE used To CReATe this project.

Please EnAble JaVaScrIpt to view the cOmments powERED by DisquS. K AThrYn rOSE faced a dileMma tyPical for a jOBbing comPoser When she sTarted out ON Her careeR. HiDe hEr cHorAL CoMpositions awaY ANd shOw Them onlY to Those wiLliNg tO paY foR tHem? Or sharE her work WiTh ThE wOrlD and eARn a pittaNcE? It WaS a cHoice BEtween RetaiNing CoNtROl oR giving ACcESS, expLaInS Ms ROse,
 the State addresses.)

In  this case , the  co dedi vide d ea ch character’s comb ined lyrics between th ree d if f eren t  c atego ries. T h es e cat eg or ies  d o n’ t hav e a ny inhe rent meani ng,  b ut each refl e ct  certain w ord s that are d ist inctiv etot hat c ate gory.  For e x ample,  Group 2is most s t ronglya s so ciat ed with Angelicaa nd Eliza, and istypifi ed by w ord s s uch as “sa ti sfied ”  and “s i st e r ”  —t hough it also  features r e fra ins fr om  ot h er cha racte rs lik e “must” an d “n ice.”

Th e deta i ls arel ess imp ortant  th antheb ig  p ict ur e , w h ic h i s  th at the a lg or ithmimme d i a t elypeg s Al exand er Hamilton’s  ly rics asdisti nctiv e fr om  therest o f the  c ast. He dominat es h is catego ryin  awa y tha t  no on e e lse  does:
Charac t erGr o u p  1  Group 2 Group 3 A ng e l ic a Schuyl er 3.9 6 %64 . 97% 31.0 8%  Aa ronB u rr 30 .16% 40 .46% 29. 39% El i za be th Schu yl er 8.5 4%  54 .03 % 37. 4 3% E nsembl e 35.40%  33.9 7 % 30.63% Extra s  4.35% 68.45% 27 . 1 9% King  Ge orge 2.95%35.8 7 % 61 .18% Al exa n der H amilt on 93 . 28% 3.51% 3.21% Tho ma s Jeff erson  5.08% 57.72%  37.20% La f a y ette 2.6 3% 1 8 .9 0 % 78.47% JohnLaure ns 4.53% 2 4 .70% 70. 77% J ames Mad ison 3.50 % 47.36% 4 9.1 4%Her cul e s  M ulligan 2.32% 1 7.42 %80.26%Phi lip Ham ilt on2.9 0%36 .27% 60.8 3% Georg eWa sh in gto n 5.6 3 % 50.86%  4 3.5 1% 

T he ma n  is si mplynon- st op . 

( Can weg et b ackto  p olit ics ,pleas e? F o r a  differ ent takeont he t im eperiodcovered in “H am ilton,” ch ec k o ut mye s say on t he Election of 1800: “1 8 00 andthe  poli tics  of the a bso lute.” )
V isit m y GitH ub to  v iew t he co d e  u sed  t o create t h i s p rojec t. 

Pl ease en able JavaScript to vie w the comment s p ower e d  b y D i sq u s.KA THR Y N  ROSE faced a d ilemma typ ic a lfor a jobb i ngco mposerwhen sh e sta r ted out  on her c areer. Hi de  her c hor al composi tions aw ay and sh o w the m  o nly t o t hose w il lin g topay fo r  th em? O r shar eher wo rk wi th  the wor ld and earn a pittance?  It wa s a c hoi ce betwee n r e tai ning cont rolor givingacc ess , exp lai n s MsRose , 
 the_State addresses.)

In_this case, the code_divided each_character’s_combined lyrics_between_three different categories._These categories don’t_have any inherent meaning,_but each reflect_certain_words that are distinctive to that category. For example, Group 2 is most strongly_associated_with Angelica_and_Eliza,_and is typified by words_such as “satisfied” and “sister”_— though_it also features refrains from other characters like_“must”_and “nice.”

The details_are less important than the big picture, which is_that the algorithm immediately pegs Alexander_Hamilton’s lyrics as_distinctive_from_the rest of the_cast. He dominates his category in_a way that no one else_does:

Character Group 1 Group 2 Group 3_Angelica Schuyler 3.96% 64.97% 31.08% Aaron_Burr 30.16% 40.46% 29.39% Elizabeth_Schuyler 8.54%_54.03% 37.43% Ensemble 35.40% 33.97%_30.63% Extras 4.35%_68.45% 27.19%_King George 2.95%_35.87% 61.18% Alexander Hamilton 93.28% 3.51%_3.21% Thomas Jefferson_5.08% 57.72% 37.20% Lafayette 2.63% 18.90%_78.47%_John Laurens 4.53%_24.70%_70.77%_James Madison_3.50% 47.36% 49.14%_Hercules_Mulligan 2.32%_17.42%_80.26% Philip Hamilton 2.90% 36.27% 60.83%_George_Washington 5.63% 50.86% 43.51%

The man is simply_non-stop.

(Can we get back_to_politics, please? For a_different take on the time_period covered in “Hamilton,” check out_my essay_on the_Election of 1800: “1800 and the politics of the absolute.”)

Visit my_GitHub to view the code used_to create this project.

Please_enable JavaScript_to_view the comments_powered_by Disqus._K ATHRYN ROSE faced a dilemma typical_for a_jobbing composer when she started out_on her career. Hide_her_choral compositions away and show them_only to those willing to pay_for them? Or share her_work_with_the world and earn a_pittance? It was a choice between_retaining control or_giving access, explains Ms Rose,