Here is my little contribution which is based on Chris Wilson's Wolfram Summer School side project about "Book Colors".

• Gather a list of Color Names:

  colornames = {" alice blue ", " antique white ", " aqua ", " aquamarine ", " azure ", " beige ", " bisque ", " black ", " blanched almond ", " blue ", " blue violet ", " brown ", " burly wood ", " cadet blue ", " chartreuse ", " chocolate ", " coral ", " cornflower blue ", " cornsilk ", " crimson ", " cyan ", " dark blue ", " dark cyan ", " dark golden rod ", " dark gray ", " dark green ", " dark khaki ", " dark magenta ", " dark olive green ", " dark orange ", " dark orchid ", " dark red ", " dark salmon ", " dark sea green ", " dark slate blue ", " dark slate gray ", " dark turquoise ", " dark violet ", " deep pink ", " deep sky blue ", " dim gray ", " dodger blue ", " fire brick ", " floral white ", " forest green ", " fuchsia ", " gainsboro ", " ghost white ", " gold ", " golden rod ", " gray ", " green ", " green yellow ", " honey dew ", " hot pink ", " indian red ", " indigo ", " ivory ", " khaki ", " lavender ", " lavender blush ", " lawn green ", " lemon chiffon ", " light blue ", " light coral ", " light cyan ", " light gray ", " light green ", " light pink ", " light salmon ", " light sea green ", " light skyblue ", " light slate gray ", " light steel blue ", " light yellow ", " lime ", " lime green ", " linen ", " magenta ", " maroon ", " medium blue ", " medium orchid ", " medium purple ", " medium sea green ", " medium slate blue ", " medium spring green ", " mediumturquoise ", " medium violetred ", " midnight blue ", " mint cream ", " misty rose ", " moccasin ", " navajo white ", " navy ", " old lace ", " olive ", " olive drab ", " orange ", " orange red ", " orchid ", " pale golden rod ", " pale green ", " pale turquoise ", " pale violet red ", " papaya whip ", " peach puff ", " peru ", " pink ", " plum ", " powder blue ", " purple ", " red ", " rosy brown ", " royal blue ", " saddle brown ", " salmon ", " sandy brown ", " sea green ", " sea shell ", " sienna ", " silver ", " sky blue ", " slate blue ", " slate gray ", " snow ", " spring green ", " steel blue ", " teal ", " thistle ", " tomato ", " turquoise ", " violet ", " wheat ", " white ", " white smoke ", " yellow ", " yellow green "};
  

• Use the color Interpreter to get them in the WL:

  colors = Interpreter["Color"][colornames]
  

• Make a PieChart of the StringCounts of these color names:

  Column@Table[
  link="http://shakespeare.mit.edu/"<>works<>"/full.html";
  text=ToLowerCase[Import[link]];
  PieChart[
  ParallelMap[StringCount[text,#]&,colornames],
  ChartStyle->colors,
  PlotLabel->Hyperlink[TextCases[text,"Line",1],link],
  LabelingFunction->"RadialCenter",
  ChartLabels->Placed[colornames,"RadialCallout"],
  ImageSize->600],
  {works,StringSplit[#,"/"][[-2]]&/@Import["http://shakespeare.mit.edu/","Hyperlinks"][[3;;-8]]}]
  

Enjoy!

Attachments:

You are completely right David! Somehow I assumed that was due to the domineering qualities of red:

Red - is a bold color that commands attention! Red gives the impression of seriousness and dignity, represents heat, fire and rage, it is known to escalate the body's metabolism. Red can also signify passion and love. Red promotes excitement and action. It is a bold color that signifies danger, which is why it's used on stop signs. Using too much red should be done with caution because of its domineering qualities. Red is the most powerful of colors. The Psychology of Color

Thanks for noticing it. I've just updated the color names above:

colornames = StringJoin[" ", #, " "] & /@ colornames;

Now it looks just right:

I took this opportunity to use the Wolfram Language on XML documents, specifically a TEI (http://www.tei-c.org) version of "The Tempest," which can be found at the University of Oxford Text Archive (http://ota.ox.ac.uk).

We start by importing the xml document as an XMLObject.

tempestxml = Import["http://ota.ox.ac.uk/text/5725.xml", "XMLObject"];

After exploring the document a little, I found that I could extract lines by a specific speaker with Cases. Here is how to get all the lines spoken by Prospero:

proslines = 
  Cases[tempestxml, XMLElement["sp", {}, {XMLElement["speaker", _, {"Pros."}], line_}] :> line, Infinity];

I made a WordCloud of those lines, only to discover that we lack Elizabethan stopwords:

WordCloud[DeleteStopwords@ToLowerCase@TextWords[StringRiffle[Flatten[proslines//.XMLElement[_,_,content_]:>content]]]]

It's much more satisfying after a minor tweak:

WordCloud[DeleteCases[DeleteStopwords@ToLowerCase@TextWords[StringRiffle[Flatten[
    proslines //. XMLElement[_, _, content_] :> content]]], "thee" | "thou" | "thy"]]

Why limit ourselves to one character, though?

linesbychar = First@First@# -> Last /@ # & /@ GatherBy[Cases[tempestxml, 
    XMLElement["sp", {}, {XMLElement["speaker", _, {char_}], line_}] :> char -> line, Infinity], First]; 

Grid[Partition[Column@{First@#, WordCloud[DeleteCases[DeleteStopwords@ToLowerCase@TextWords[
               StringRiffle[Flatten[Last@# //. XMLElement[_, _, content_] :> content]]], 
                    "thee" | "thou" | "thy"]]} & /@ linesbychar, 6], Frame -> All, Alignment -> Left]

We don't have to limit ourselves to characters, we can make WordCloud for each scene. In this document, each scene is contained in a <div>

scenes = Cases[tempestxml, XMLElement["div", _, div_] :> div, Infinity];
Grid[Partition[Column[{First@#, WordCloud[DeleteCases[DeleteStopwords@ToLowerCase@TextWords[
           StringRiffle[Flatten[Cases[Last@#, XMLElement["ab", _, line_] :> line, Infinity] //. XMLElement[_, _, content_] :> content]]],
             "thee" | "thou" | "thy"]]}] & /@ ((Replace[Flatten[Cases[#, 
                XMLElement["head", _, h_] :> (h //. XMLElement[_, _, content_] :> content)]], {s_String} :> s] -> Rest@#) & /@ scenes), 5],                 
                    Frame -> All, Alignment -> Left]

@Marco Thiel , you are indeed fast. My take on the graph of characters, leaving the conversations between characters.

data = SemanticImport[
   "c:\\Users\\Diego\\Downloads\\will_play_text.csv", Automatic, 
   "Rows", Delimiters -> ";"];
data[[All, 
    4]] = (ToExpression@StringSplit[#, "."] & /@ 
     data[[All, 4]]) /. {} -> {Missing[Empty], Missing[Empty], 
     Missing[Empty]};
sentiment = 
  Classify["Sentiment", data[[All, 6]]] /. {"Neutral" -> 0, 
    "Positive" -> 1, "Negative" -> -1, Indeterminate -> 0};
data[[All, 6]] = Transpose[{data[[All, 6]], sentiment}];
ds = Dataset[
  AssociationThread[{"ID", "Play", "Phrase", "Act", "Scene", "Line", 
      "Character", "Text", "Sentiment"}/@ (Flatten /@ data)];
getEdges[play_, act_, scene_] := 
 DirectedEdge[#[[1]], #[[2]]] & /@ 
  Partition[
   ds[Select[#Play == play &] /* Union, {"Act", "Scene", "Phrase", 
       "Character"}][Select[#Act == act && #Scene == scene &], 
     "Character"] // Normal, 2, 1]
getGraph[play_] := 
 Graph[Flatten[
   getEdges[#[[1]], #[[2]], #[[3]]] & /@ (ds[
         Select[#Play == play &] /* Union, {"Play", "Act", "Scene"}] //
         Normal // Values // Most)], VertexLabels -> "Name", 
  ImageSize -> 1024]
getGraph["Othello"]

Well, what about how many movies or TV Shows were made of the most famous plays?

plays={"Macbeth", "Romeo and Juliet", "Othello", "Hamlet", "King Lear", \
"Richard III", "The Tempest", "Merry Wives of Windsor", "Titus \
Andronicus"}
movies[play_] := 
 Import["http://www.imdb.com/find?q=" ~~ 
    StringReplace[play, " " -> "%20"] ~~ 
    "&s=tt&exact=true&ref_=fn_tt_ex", "Data"][[4, 1]]

shakespeareMovies = {Length@movies[#], #} & /@ plays // Sort // 
   Transpose;
BarChart[#[[1]], BarOrigin -> Left, ChartLabels -> #[[2]], 
   Frame -> True, 
   PlotLabel -> "Shakespeare Based Movies"] &@shakespeareMovies

BarChart[#[[1]], BarOrigin -> Left, ChartLabels -> #[[2]], 
   Frame -> True, PlotLabel -> "Shakespeare Based Movies", 
   PlotTheme -> "Detailed", ImageSize -> Large] &@shakespeareMovies