Re:Heavy Metal and Natural Language Processing
by SwellJoe on Sunday July 03, 2016 @03:04AM
(#12025085)
The "least metal words" almost seems like a challenge. I now feel strangely compelled to write metal songs with those words, and I haven't written a metal song in 20 years. I may not be particularly metal, anymore.
Carnivore (Pete Steele's band before Type O Negative) also did it in 1987 (although it's probably not going to be classified as a metal song unless your name is Marcel Duchamp or Tracy Enim): https://www.youtube.com/watch?v=eGDTrWw_APM
Re:Heavy Metal and Natural Language Processing
by SwellJoe on Sunday July 03, 2016 @01:37PM
(#12026856)
The lyrics for the first couple of Carcass albums were pulled from medical texts, so I think lyrical content is pretty flexible. But, the nonmetal words almost sound like a very boring BBC program about people in an office in the 19th century; like Eastenders with stuffy accountants. It'd be an interesting challenge.
Re:Heavy Metal and Natural Language Processing
by amelius on Sunday July 03, 2016 @06:05AM
(#12025458)
> In the face of this complexity, it is not surprising that understanding natural language, in the same way humans do, with computers is still a unsolved problem.
I have the feeling it is not just an unsolved problem, but also an undefined problem.
Re:Heavy Metal and Natural Language Processing
by Bahamut on Sunday July 03, 2016 @02:28AM
(#12025026)
I noticed there was song data errors in places - for example, in this chart (http://www.degeneratestate.org/static/metal_lyrics/clusters....), in the most representative songs for Tiamat, it lists White Pearl, Black Oceans. That is a song by Sonata Arctica. It also lists 3 versions of the same song for Nightwish in most representative songs (Elan). Similar mistakes are made for various bands in that chart (Symphony X, Hammerfall, Therion, Sabaton, Stratovarius, Helloween, Within Temptation, etc.).
This was interesting to read in general though, especially as someone who listens to metal quite frequently (mostly of the power metal variety).
Re:Heavy Metal and Natural Language Processing
by simonbw on Sunday July 03, 2016 @02:35AM
(#12025038)
He mentioned that in the article:
> What's interesting is that while the most representative songs for each band are mostly their own songs, occasionally other bands songs creep in. For example, "Wrathchild", is an Iron Maiden song, not a Diamondhead song.
I don't think it's a problem with the song data. When the algorithm picks a song it considers most lyrically representative of a band, it chooses from all the songs in the dataset, and it doesn't always pick a song by that band.
Re:Heavy Metal and Natural Language Processing
by abecedarius on Sunday July 03, 2016 @02:50AM
(#12025064)
That list of most and least metal words, if I didn't know where they were from, I'd have guessed was the 19th-C. Romantic reaction to the Enlightenment (except for a few anachronisms like 'gonna'). How metal was Edgar Allan Poe?
Re:Heavy Metal and Natural Language Processing
by tragic on Sunday July 03, 2016 @06:00AM
(#12025440)
There's also Ahab, who based a fantastic album on the narrative of Arthur Gordon Pym[0]. It's sort of halfway between funeral doom and your Isis/Neurosis style stuff.
Re:Heavy Metal and Natural Language Processing
by mkozakov on Sunday July 03, 2016 @08:09AM
(#12025760)
There is something to be said about song structure. Most songs will have a chorus, which is repeated throughout the song. Choruses will generally have higher 'readability' because they are meant to be more memorable. If you don't pre-process your corpus by removing repeating paragraphs, then your comparisons to the brown corpus are not as valid, since brown doesn't use repetition.
Re:Heavy Metal and Natural Language Processing
by jessemoeller on Sunday July 03, 2016 @11:28AM
(#12026311)
I believe that the convention on darklyrics is to either elide or replace with "[chorus]" all repetitions of the chorus after the first. A few spot checks seem to confirm this, but I can't find an official policy on it.
Re:Heavy Metal and Natural Language Processing
by 1ris on Sunday July 03, 2016 @02:27PM
(#12027066)
I'm suprised no difference between american and european metal was found. I feel like they are quite different, but it seems this is not supported by this data.
I found it amusing that Venom and Running Wild are grouped together in the first step. But well, by the lyrics it fits. The rest matches my expectations surprisingly well.
Re:Heavy Metal and Natural Language Processing
by bionsuba on Sunday July 03, 2016 @01:51AM
(#12024965)
Fantastic article.
I wonder if the clustering method can be used/is used by apps like Spotify to create a list of "related bands", as the graph at the end was fairly accurate.
[The] academic chairman noted [the] secretary['s] considerable measurements. /
University employee[']s attorney indicated [the] administrative committee. /
Literary agencies particularly fiscal. /
drums come in
rhythm guitar and bass come in
main riff begins
begin song
Carnivore (Pete Steele's band before Type O Negative) also did it in 1987 (although it's probably not going to be classified as a metal song unless your name is Marcel Duchamp or Tracy Enim): https://www.youtube.com/watch?v=eGDTrWw_APM
(which may not be particularly metal, but they have an interesting contrast between their sound and their lyrics)
https://www.youtube.com/watch?v=VPST2nE0KIo
The lyrics are a german recipe on how to make cookies without eggs....
I have the feeling it is not just an unsolved problem, but also an undefined problem.
This was interesting to read in general though, especially as someone who listens to metal quite frequently (mostly of the power metal variety).
I don't think it's a problem with the song data. When the algorithm picks a song it considers most lyrically representative of a band, it chooses from all the songs in the dataset, and it doesn't always pick a song by that band.
LSTMetallica: Generation drum tracks by learning the drum tracks of 60 Metallica songs https://keunwoochoi.wordpress.com/2016/02/23/lstmetallica/
Keunwoo is also the translator of Deeplearning4j's Korean site: http://deeplearning4j.org/kr-index
[0] https://en.m.wikipedia.org/wiki/The_Giant_(Ahab_album)
Given a text it will split each word, add up the metalness score and divide it by the number of words.
I found it amusing that Venom and Running Wild are grouped together in the first step. But well, by the lyrics it fits. The rest matches my expectations surprisingly well.
[1] https://youtu.be/wpe8eNdpAiM?t=307
I wonder if the clustering method can be used/is used by apps like Spotify to create a list of "related bands", as the graph at the end was fairly accurate.