Freddie Mercury
Following the suggeston posted by Mohamed Magdy about the Queen Image, here you have a Freddie Mercury portrait with the most used words of his songs.
A closer look.
Download PDF JO-D-100310-Freddie-ENG (205kB)
Kurt Cobain
We are talking about music again. Now we will look at Nirvana lyrics. Kurt Cobain portrait as base imagen.
Most used words: like (63), go (48), take (45), want (40), yeah (39), make (36), never (36), can (34), know (33), get (32), just (32), hey (31), got (30), one (30), see (30), oh (29), say (29), happy (28), way (28), heart (27), think (24), find (23), something (23), day (22), night (21), time (21), come (20), die (20), eyes (20), now (20), shame (20), wish (20), away (19), friend (19), new (19), said (19), love (18), back (16), dive (16), hurts (16), else (15), feel (15), nothing (15), pick (15), ain’t (14), cross (14), kiss (14), light (14), mean (14), cold (13), help (13), hung (13), keep (13), look (13), sun (13), wait (13), another (12), eat (12), face (12), fun (12)…
More information about Nirvana and Kurt Cobain in Wikipedia.
Download free pdf (for non comercial use):
JO-D-100301-Nirvana-ENG.pdf (173kB)
Freddie Mercury words
The most used words in Queen lyrics. The base image is the cover art of one of their albums.
Thank you Freddie…
Download JO-D-100301-Queen-ENG.pdf (205kB)
Files free for non commercial use.
Obama Words Relationship
Obama speeches are a good testing ground. This time we are going to investigate the relationship between some of the most ‘representative words’ used in his speeches. These words are: american, can, country, hope, iraq, people, schools, war, work and world. You can find the Obama Speeches in this url http://obamaspeeches.com/
The words cloud image represents the words close to one of the ‘representative word’ (in color). Word size represents the number of times the word appears. The distance to the ‘representative word’ represents the average distance of all instances of each word.
Take a closer look.
The last image represents all the words with a proportional size and an evolution in time of the most used words. Also, there is an attempt to count only the adjetives (it is just an experiment!), because, in some way, adjetives ‘describe’ things.
You can download and distribute my images for free (for non commercial use).
Download PDF ObamaDistances.pdf (8.5MB)
Download PDFÂ ObamaEvolutiom.pdf (385kB)
Remember my other post related to Obama. The Obama Speeches Portrait, and the Obama Tree.
Zapatero and Rajoy
Just a quick post showing another point of view of the last congressional debate in Spain.
It is a little bit hard to compare, because Zapatero speak much more than Rajoy (17935 / 4702 words).
Speeches from http://www.congreso.es/.
Download free JO-D-100224-ZapateroRajoy-ESP.pdf (283kB)
Jack VS Locke
Animated by a suggestion of ^BadNumber^ from lostzilla.net here we go again with… Lost.
This time I have analyzed all the dialogues of the five seasons, but thanks to the scripts hosted in lostpedia.wikia.com we can know the words each character said… exactly.
So here are the results. The first image is a versus between Jack and Locke. Each face has the words correspondig to the character.
In the last image each character has his own words. The bigger the more times they used it. The distance to the character is mostly random. We choose nine of them… look at it and you will know why.
As a curiosity take a look to number of times each character talk, the number of words spoken and the average words per intervention.
jack (3254 int / 28951 words / 8.89 avg)
locke (2339 int / 22670 words / 9.69 avg)
hurley (1761 int / 17244 words / 9.79 avg)
kate (2272 int / 17327 words / 7.62 avg)
sawyer (2387 int / 24165 words / 10.12 avg)
sayid (1505 int / 14754 words/ 9.80 avg)
ben (1009 int / 13064 words / 12.4 avg)
juliet (924 int / 8661 words / 9.37 avg)
desmond (868 int / 8413 words / 9.69 avg)
I want to thank ^BadNumber^ and cesarfuenla for their assistance with ideas and suggestions. Thank you guys… hope you like it.
Download free PDF jackvslocke.pdf (578kB)
Download free PDF lost_thewordstheysaid.pdf (6.5MB)ï»ż
As a bonus… the ‘dark’ version…
Download free PDF jackvslocke-dark.pdf (189kB)
You can print it freely (for non commercial use), but if you prefer a big size printed copy you can order it in deviantart:
Jack vs Locke
Lost The Words They Said
Jack vs Locke Dark Version
Millenium Characters
Once again we will work with Millenium trilogy, the Stieg Larsson Bestsellers.
This time I covered the three books searching relationships between some of the main characters and the words around it.
Word size represents the numbre of times the word appears. The distance to the name represents the average distance of all instances of the each word.
Download PDF MilleniumCharacters-ENG.pdf (4.5MB)
Take a closer look.
John Maeda
Just a quick post about John Maeda and his book ‘ Laws of Simplicity” wich I recommend reading.
The image contains the most used words in this book.
And, of course, thanks John because you began processing….
Download free PDF JohnMaeda.pdf (580kB).
Take a closer look.
Lost Characters.
We are Lost again. Now we will look at the Characters evolution and their relationship.
I took all the english subtitles of the five seasons and I counted all the words but in a new and different way, by taking care of word position.
The right part of the image shows the number of times the word appears through episodes. You can see how some characters just disappear or gain prominence in time.
In the left part of the image, every character has the words that are close to his name in the subtitles. The bigger, the more times the word appears. The distance to the character is proportional to the distance between words. Characters are colored in order to ease location. The character position is random.
You can draw some conclusions.
Take a closer look….click to enlarge.
Hope you like it!
Download PDF poster LostCharacters.pdf (883kB).
The Bible
Probably the most interesting book ever… The Holy Bible.
This time I want to know if there is deep differences between New and Old Testaments. So I counted words from these documents independently and put the results together each one in a side of the image. The left hand represents the words from the New Testament. The right hand the Old Testament ones.
Because the Old testament is bigger than the New Testament I used a correction factor so the words can be compared in size between both documents.
As base image I used a part of a well know Michelangelo painting…
One more thing. There is a lot of free versions of the Bible. I just pick one of them in english and another in spanish (double work of course). There must be huge differences between all the different versions of the Bible. Even between the differents idioms. For example the word ‘Lord’ in english correspond to ‘Jehovah’ in the spanish version… I don’t know why…
And remember, it is just a exercise… the results may be inexactly or totally wrong at all..
Old Testament: lord 7279, god 3340, son 3044, said 2941, king 2759, upon 2502, israel 2501, day 2124, house 1943, people 1910, man 1829, land 1709, hand 1681, children 1662, saying 1628, came 1590, shalt 1510, come 1432, one 1431, go 1342…
New Testament: god 1392, said 1061, jesus 984, man 908, saying 904, things 848, lord 734, come 657, one 615, also 599, christ 573, came 506, day 485, son 454, father 428, now 407, men 396, therefore 356, know 348, went 338…
Download TheBible.pdf (English Version) (436kB)
Download LaBiblia.pdf (Spanish Version) (440kB)
Gilmore Girls
Yes, I know… after seven seasons the show is over but it is so funny and the words are important within so many fast dialogues…
So I decided to collect all the words of the seven seasons with this result:
rory 2931, thank 2678, mom 2096, sorry 1964, luke 1877, guys 1805, lorelai 1566, nice 1469, kid 1112, dad 962, paris 881, hello 851, dinner 848, coffee 807, tonight 752, stuff 727, tomorrow 657, gilmore 644, date 643, sookie 643, lane 629, kirk 628, dean 624, logan 572, late 571, hate 551, perfect 546, married 540, emily 534, richard 512, taylor 510, excuse 505, drink 494, yale 491, crazy 478, jess 474, parents 473, phone 473, weird 468, grandma 463…
Perhaps some of them are part of the four final words that we are still waiting for…
And what about Lauren Graham?… as lovely in person as being Lorelai Gilmore.
Get LostâŠ
We are close to the last season of Lost. It’s time to review all the words used in the last 5 seasons… perhaps we can find a clue about the end of the story…
First words: jack 906, sorry 578, john 547, thank 465, guy 403, kate 372, hell 371,locke 342, sayid 328, claire 282, ben,280, charlie,270, sawyer,269, dude 267, walt 203, damn 196, michael 196, beach 192, hurley 184, hello 180, jungle 172, dad 171, desmond 155, trust 153, jin 152, juliet 152, crash 150, anyone 147, ain’t 145, kid 143, hurt 142, worry 140, hugo 139, nice 137, hatch 136, whatever 135, real 131, camp 130, excuse 129, stuff 120…
As you can see Jack is the first one… but I believe that John is the true soul of the show… it had to be you John Locke…
Juan Carlos I christmas speeches
The most commonly used words in the 34’s Christmas speeches of King Juan Carlos I, from 1975 to 2008.
First 20 words: españa 244, españoles 236, debemos 186, familia 143, paz 130, esfuerzo 118, mejor 117, unidad 104, futuro 102, social 98, sociedad 98, pueblo 92, mayor 90, nuevo 86, trabajo 84, comĂșn 82, econĂłmica 82, constituciĂłn 81, deseo 81, paĂs 80…
This time only spanish version.
Radiohead
Words from 89 Radiohead lyrics and a Thom Yorke photo.
Most used: get 73, can 67, like 58, just 57,come 53, got 52, want 46, go 38, one 38, try 38, feel 36, eyes 34, nothing 34, back 33, now 31, everything 30, know 30, take 30, run 27, stand 27, head 26, never 26, see 25, think 25, better 24, case 24, best 23, things 23, time 23, man 22, wish 22, even 21, happen 21, little 21, sit 21, turn 21, world 21, end 19, love 19…
Download PDF radiohead-eng.pdf (190kB)
Obama tree.
We will go a step forward from this Obama Post. Remember in that case I counted all the words in more than one hundred Obama speeches.
Using the same data I made a new algorithm based on a simple tree structure but, in this case, the branches are the words.
The tree structure is highly random, but the size o the words (branch width) is always proportional to the number of times the word appears in the speeches.
The image contains four samples with different color variations. The branches take their color from the original Obama photo post. I added some transparency so the overlap generates more color variations.
The algorithm generates a new random version of the tree every time I click. So I just clicked till I obtained a nice one and then saved it as pdf.
Download obama-tree.pdf 5.34MB
Also take a look at this other version. Generated using the Van Gogh Image results.
Hope you like it!.
Jester in words.
Marillion was one of my favorites bands… while Fish was in there.
In the Fish era all the albums covers was made by Mark Wilkinson. Really beatifull drawings that match perfectly with the introspective songs lyrics.
Let’s go. I mix both concepts counting all the words from Marillion (with Fish) lyrics and one of their album covers.
The first most used words: know 80, just 67, heart 52, time 47, got 43, way 41, now 39, eyes 38, never 37, say 34, another 33, love 33, go 32, take 31, like 30, night 30, away 29, tux 29, want 28, burning 27.
Download PDF version. (347kB).
Download (white) PDF version. (347kB). (see png)
(Dedicated to Rafa, Chavo and Pez… so, so, so close to forty)
Hey BossâŠ
Words from more than four hundred Bruce Springsteen lyrics. This time the word size is proportional to the number of times the word appears in his songs.
The list: just 733, well 720, night 667, baby 650, come 650, got 621, love 604, now 582, like 516, man 484, little 467, girl 419, oh 417, know 414, get 402, go 383, one 353, can 347, day 345, ain’t 331…
The original image is the cover from one of their latest albums.
Lisbeth gaze
Download (English) millenium-eng.pdf (215kB).
Download (Spanish) millenium-esp.pdf (235kB).
Based in the Stieg Larsson Bestseller. All the words from the Millenium trilogy.
In this case the problem was the huge step between the first words. Take a look at the word list:
blomkvist 2521, salander 2376, police 879, vanger 878, berger 703, zalachenko 589, murder 584, lisbeth 526, investigation 475, apartment 468, harriet 453, bjurman 448, bublanski 405, mikael 396, armansky 379, millennium 356, security 345, coffee 322, martin 319, computer 309, wennerström 307, ekström 302, svensson 300, henrik 288, niedermann 287, spent 286, taken 284, giannini 270, photograph 268, desk 267.
In only 30 words we have tenth of the initial size. The words becomes like ants quickly.
So I decided to insert a kind of ‘brake’ in the algorithm that makes the steps between words shorter. Yes, we lose accurate proportionality but still retain importance degree between words.
Images free for personal use only.
The spanish version uses the spanish books. No google translation.
Let there be House
Download house-eng.pdf (234kB).
Download house-esp.pdf (264kB).
House MD is one of my favorites TV show, I really enjoy the show. House deserves a tribute!.
Hands on it, I collected all the subtitles of the first five seasons from the website http://www.tvsubtitles.net/ something like 115 episodes.
Because the subtitles are âcommon languageâ there is not so much information as in a song or a speech. I needed to clean up the word list in order to have a significant result.
So I increased the numbers of words that the algoritm will discard during the counting process
using the 1000 most commonly used English words that I found in http://www.duboislc.org/EducationWatch/First100Words.html
I also realized that there is a lot of strange words with only one hit, probably due to the nature of the subtitles. I decided to include only the words with two or more hits.
Here is the final image!⊠Cuddy would be proud.
As usual, all files for personal use only.
Goodbye MichaelâŠ
Download Michael Jackson (English version).pdf (190kB).
Download Michael Jackson (Spanish version).pdf (188kB).
This is an updated version of my âgoodbye messageâ for Michael Jackson. This time I collected more than four thousand messages that people posted in http://www.lacortedelreydelpop.com/ after the sudden death of Michael.
All the messages were written in Spanish so, in this case, the English version is a direct translation of the spanish using Google.
I did not discrimĂnate. The image contains all the words of all the messages⊠even the ânot so niceâ ones.
20 first words:michael 3158, always 2734, music 1509, king 1423, jackson 1173, pop 1164, heart 1158, life 1023, thanks 919, never 870, world 869, peace 820, best 40, god 725, left 723, big 716, dead 664, rests 656, believe 651, alone 648.
My apologies because I cannot find the website that hosted the original image. It was a very nice abstract image of an asian artistâŠ. I think.
Those images are free for non-commercial personal use.
Play it again, Eddie.
Same approach as the Obama post.
Download Iron Maiden (English version).pdf (195kB).
Download Iron Maiden (Spanish version).pdf (174kB).
In this case I used more than one hundred and fifty lyrics from Iron Maiden songs over an image of Eddie The Head, the well know band mascot (one of these incredibles Derek Riggs illustrations). The font used is Metal Lord, based in the band logo.
As usual you can use these files only for personal use, and you cannot modify them.
First 20 (of 2088) more often words in the lyrics: just 175, time 169, know 165, see 143, come 138, live 137, now 137, can 133, feel 127, get 114, take 113, dream 110, look 105, got 103, tell 103, go 101, life 99, world 97, make 95, like 89.
Other links:
Derek Riggs homepage.
Iron Maiden homepage.
(a tribute to âRafa the Headâ)
Obama Speeches
Click in the image for high resolution. You can also download the PDF English version or the Spanish one. In the PDF version the words are vectors so you can print it in a very large format (ISO-A1 or so) to have a nice poster for your office/room.
Download Obama Speeches (English version).pdf (360kB).
Download Obama Speeches (Spanish version).pdf (360kB).
All those images are free for non-commercial personal use. You can distribute freely, but you cannot modify them in any way. Please refer to this site if you redistribute them.
Inspired by the excellent work of Jeff Clark (http://neoformix.com/) I coded an algorithm in Processing that transform images in words in different ways.
I have tested it for a few months and now I want to share the results with you. This is the first example of a series of texts art images that Iwill be posting in the next weeks. Here is the processâŠ
First I took the transcriptions of more than one hundred Obama speeches from the website http://obamaspeeches.com/. Then I counted all words using an adapted version of the Snowball stemmer for Processing (thanks to Lot!, http://feenelcaos.org/). I also picked up an Obama photo using Google Image to find a good one.
Finally I use my âimage pattern searcherâ algorithm (I have to find a name for it) to fit all the words in the image. In this case the more times the word appears in the speeches the bigger it is (proportionally).
Here you have an example of the 20 more often words in the speech: Â can,785, american,683 , people,648, one,619, time,561, years,548, country,521, work,481, make,477, need,454, just,440, know,415, america,399, go,384, come,356, care,355, nation,352, now,345, like,343, right,332.
In order to get a nicer result I also let the algorithm reuse words but only in a smaller font size.
Note that the Spanish version of this image used a direct Google translation of the English words. So some of them may sound like âSpanglishâ or have a bad translation because Google cannot take care of the context.