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foreign

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hi everyone thank you for being here

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this is the last talk before we start uh

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well before we all enter Chris and

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oigabauer's sardom of lightning talks

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there there is afternoon tea first so

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stock up

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um

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in the meantime Amanda Hogan is not only

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a teacher she's also a teacher teacher

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so she teaches teachers how to teach her

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in Computing

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yes that's what yes Esther that is what

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yep good great

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um if you've been on the Discord she's

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also the person that's putting up all of

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those really cool visual diagrams of

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talks they're awesome go and find them

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yes

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yes I'm just gonna let it do the thing

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yeah thanks very much Maya okay hello

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everybody

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um I know that you thought this would be

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the quiet talk when nobody picks on you

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and you can just hide at the back of the

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theater but that's not what this clock

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is going to be because I'm really

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nervous and I need to use audience

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participation so there's going to be

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some audience participation in here

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somewhere are we singing cilia am I the

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right person to find out we have no idea

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let's find out okay so why am I doing

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this talk

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thank you

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I am the luckiest woman in the world uh

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every morning I am woken up with a

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coffee delivered to the side of my bed

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I'm not kidding this is a photo of my

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side of my bed and I I love my husband

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and he's never allowed to go anywhere

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because I need this

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um so every morning we sit down and we

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drink our coffee and we play hurdle

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hurdle is a game that came about when

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when Josh Wardle did the whole world

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thing and there were a million clones

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made and hurdle was a thing where you've

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got 17 seconds of audio to identify a

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song and then once herder was was made

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then there were some spin-offs that were

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also made and they were decade based

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hurdle was bought by Spotify and then

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immediately killed and so we stick with

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hurdle 80s 90s and 2000s

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and I thought

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um there's an interesting thing it's an

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like uh it's interesting that my husband

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and I uh we identify songs differently I

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work with lyrics he works with music and

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bands and so it's in my mind okay

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foreign

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we also are a family of Music listeners

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we listen to music while we make dinner

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we all have headphones we listen to

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music and our commutes

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um we all have overlapping interest but

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also diverse interests my parents were

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from the age that you know the big pop

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decades the 60s and 70s I was Peak

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listening in the 80s and 90s and my

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children are Peak listening in the

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post-2000s world so there's a friendly

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competitiveness between the generations

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which generation is the best generation

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of music

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interesting question I have rattling

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around in my head

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I used to do data stuff for a living a

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long time ago I don't want to

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misrepresent myself here I was not a

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data Legend but I was a dashboard

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builder at Microsoft a long time ago it

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was a ton of a time ago now but and I

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thought

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that I could do with updating my data

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skills

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I have heard about libraries like pandas

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and I wanted to have a play but when do

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you find time to play you never find

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time to play so I

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like the two things started to mesh

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together could I use this as an example

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of how to learn something new while

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trying to find out something interesting

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you know something that I'm interested

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in can I finally tell my children that

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their generation of pop music is worse

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than my generation of pop music okay

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so confident confident that you can't

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get deeper than the lyrics of Nirvana

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and Mariah Carey I set out so I had to

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have a whole lot of procedure involved I

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had to make some decisions and there are

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some decisions that some of you will

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disagree with but I went with those

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decisions anyway and you're welcome to

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go ahead and do your own procedure but

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we'll we'll work through my procedure at

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the time being so I needed to find some

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I needed to make some decisions the

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first decision was what music what music

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do I sing in the car what's music do my

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kids dance around the house to what

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music I went with pop music because it's

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the thing we sing most often and then I

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wanted to find Trends I wanted to find

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some music to analyze and so I went to

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billboard.com billboard is a magazine

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that's been around since the 1800s they

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are the ones that publish the number one

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songs

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um of of a year they have the hot one

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they have hot 100s

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um it is an American Magazine so there

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is a little bit of

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um consternation there because it's how

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Americans sing pop music and ours is

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slightly different but but Aria charts

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aren't comprehensive enough and so I

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needed to pick something that had a good

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list of data

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okay so I've gone with I've gone with um

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pop and I'm okay with American Music so

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I went to billboard thinking that I

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could find all of the pop songs that

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they'd ever hit uh that had ever hit

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number one and see if I could grab it

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from their website and of course I

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couldn't navigate it at all it's just

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very difficult to find anything so

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handily and I use this term with love

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there are some nerds who put some stuff

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on the internet

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so Wikipedia has a full list of all of

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the billboard number one singles of the

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70s 58 to 69 the 80s and 90s 2000s 2010s

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I did not go into the 2020s

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because it's just not a full decade so

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I think it's great that there's also

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consistency for the most part these tape

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these pages are laid out very well and I

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didn't want to go and copy and paste all

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of that stuff manually so I went and

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discovered that pandas has a fantastic

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functionality that allows me to put a

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URL in and have it read the HTML of the

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page for me and then I can say go and

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have a look at table two in that page

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and grab all of that data as a data

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frame which is like saving my life here

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okay so the downside of Wikipedia is

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that those people don't call each other

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very often and so

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we had the 20 the 2000s be inconsistent

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where instead of hash I have no to main

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number instead of artists I have artists

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with an a

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and a link instead of single I have

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single with an A and A Link and instead

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of weeks at number one I have weeks at

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no dot one like it's just annoying but

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uh then there's also uh instead of reach

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number one date they had issue date and

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there's nothing I could do with that I

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just had to assume that they were the

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same date for the time being

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um I did briefly think that I could go

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to the Wikipedia

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list of Billboard Hot 100 number one

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singles of the 2000s and update it so it

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matched the list of Billboard Hot 100

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number one singles of all the other

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decades but I have actually been in open

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source flame Wars before and they're not

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very fun and I decided that I was not

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courageous enough

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okay so but thank you Wikipedia for

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giving me that data and then how do you

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get the lyrics of all the number one

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singles since 1958 well you find an API

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and there is a library called lyrics

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genius thank you very much lyrics genius

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for existing there's a website called

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genius.com and lyrics genius will allow

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you to use Python to poll

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genius.com and get about 1100 songs

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worth of lyrics

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um it's a bit of a mess to be honest uh

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I made it made my life easier because I

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didn't have to go and download 1100

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songs worth of lyrics by myself but it

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was such a mess that I remembered why I

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had exited data analysis in the first

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place in the cleaning data is a bit of a

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pain

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okay so this is what the python looks

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like you you have to get yourself a

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secret key but once you've got your

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secret key then you can search for a

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song using the single and the artist

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that which is the title and the artist

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and it will either return or not return

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there were some problems with this and I

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will talk about them next okay so my

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problems

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the first problem is that data

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codification is really hard and we don't

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all call each other or hang out on the

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internet and agree as to how we're going

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to codify things so

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this is from 1958 the Chipmunk song also

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known as Christmas don't be late in

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Brackets by let's just check this page

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this is the literal album cover David

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Seville and the Chipmunks

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however

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in wikipedia.com it is listed as the

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Chipmunk song in Brackets Christmas

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don't be late by Alvin and the Chipmunks

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feature with sorry with David Seville

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and ingenious.com it's listed as a

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chipmunk song Christmas don't be late by

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Alvin and the Chipmunks so

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that doesn't match up this happened to

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me a lot this is the kind of thing

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that I saw a lot in my Jupiter notebook

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while I was trying to run my genius.com

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um lyric search so there are a couple of

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problems here the first one was matching

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of data so slight differences in how

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artists are named slight differences in

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how songs are titled a lot of problems

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with feet or featuring and so matching

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up was a problem there was also some

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connectivity issues apparently lyrics

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genius isn't prepared for people to just

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pull it with 1100 songs worth of trying

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to get the lyrics and so I would have to

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run this and I'd keep track of

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all of the ones that had failed and run

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them again and again until I got down to

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a a core list that were actual problems

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that I had to go and do manually

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um but the rest of them were just it had

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dropped out at the wrong time it was it

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was a pain

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okay so then my other problem was even

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with songs that I'd returned the lyrics

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for I could get something like this

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this is what you get when you search for

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Gangster's Paradise by Coolio you get

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a review of Gangster's Paradise by

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Coolio by dude called the rap critic and

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I'm I'm a teacher in my normal life and

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so I had to notice I can't stop myself

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from noticing that we're going to count

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the full stops in this review they they

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go to there and then oh my goodness

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uh it's a bit of a stream of

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Consciousness thing but also like just

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punctuate please uh okay so I had some

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issues and then I had some decisions I

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00:12:24,000 --> 00:12:28,620
had to make and some of you will

275
00:12:26,160 --> 00:12:30,060
disagree with these decisions but they

276
00:12:28,620 --> 00:12:32,339
were the decisions that I made and

277
00:12:30,060 --> 00:12:36,120
you're encouraged to go and do your own

278
00:12:32,339 --> 00:12:39,420
projects and fix mine sure so the first

279
00:12:36,120 --> 00:12:41,579
problem is I love Unicode it exists for

280
00:12:39,420 --> 00:12:42,839
a reason it's a very good thing and also

281
00:12:41,579 --> 00:12:46,860
it's very

282
00:12:42,839 --> 00:12:50,459
painful and so I needed words to match

283
00:12:46,860 --> 00:12:53,240
with other words and things like

284
00:12:50,459 --> 00:12:57,180
smart inverted commas

285
00:12:53,240 --> 00:13:00,660
were causing me pain also accents were

286
00:12:57,180 --> 00:13:03,000
causing me pain and so were Spanish

287
00:13:00,660 --> 00:13:05,940
upside down exclamation marks and I know

288
00:13:03,000 --> 00:13:08,300
these things all have a place and also I

289
00:13:05,940 --> 00:13:12,420
stripped them out with abandon

290
00:13:08,300 --> 00:13:16,380
so everything has gone back to ASCII it

291
00:13:12,420 --> 00:13:18,060
was where the world started so the other

292
00:13:16,380 --> 00:13:20,339
thing were that there were some songs

293
00:13:18,060 --> 00:13:22,320
that really didn't fit into my data set

294
00:13:20,339 --> 00:13:25,980
so here's where some of the audience

295
00:13:22,320 --> 00:13:27,420
participation kicks in the first one I'm

296
00:13:25,980 --> 00:13:31,940
going to see if anybody in the audience

297
00:13:27,420 --> 00:13:31,940
knows this song by the image

298
00:13:34,139 --> 00:13:37,220
no okay

299
00:13:38,100 --> 00:13:44,399
okay

300
00:13:39,680 --> 00:13:46,920
so we had Obama it and non-english

301
00:13:44,399 --> 00:13:49,920
language songs I had to strip out

302
00:13:46,920 --> 00:13:52,079
because I was going to be analyzing the

303
00:13:49,920 --> 00:13:53,579
language from an English perspective and

304
00:13:52,079 --> 00:13:55,980
it just doesn't make any sense for me to

305
00:13:53,579 --> 00:13:57,360
apply those algorithms to non-english

306
00:13:55,980 --> 00:14:00,800
songs so there are a couple of these

307
00:13:57,360 --> 00:14:00,800
let's have a look at some others

308
00:14:00,899 --> 00:14:06,570
anyone

309
00:14:03,510 --> 00:14:06,570
[Music]

310
00:14:07,220 --> 00:14:13,620
okay so Macarena and then this one

311
00:14:11,100 --> 00:14:16,160
should be much more likely to to be

312
00:14:13,620 --> 00:14:16,160
identified

313
00:14:16,680 --> 00:14:19,700
there we go

314
00:14:20,519 --> 00:14:24,360
there we go despacito

315
00:14:22,680 --> 00:14:27,240
um and there were a couple of songs that

316
00:14:24,360 --> 00:14:29,339
were number ones but were instrumental

317
00:14:27,240 --> 00:14:31,620
and so they're not used to me at all so

318
00:14:29,339 --> 00:14:33,899
I had to strip those out uh so can we

319
00:14:31,620 --> 00:14:35,760
see if we can identify our instrumental

320
00:14:33,899 --> 00:14:37,680
number ones it's actually really unusual

321
00:14:35,760 --> 00:14:40,320
for a number uh for a song to get to

322
00:14:37,680 --> 00:14:42,240
number one as an instrumental except

323
00:14:40,320 --> 00:14:46,139
especially recently it was much more

324
00:14:42,240 --> 00:14:48,420
common in the in the earlier uh decades

325
00:14:46,139 --> 00:14:50,660
um so this is one anybody know what this

326
00:14:48,420 --> 00:14:53,060
might represent

327
00:14:50,660 --> 00:14:55,100
Rocky yeah

328
00:14:53,060 --> 00:14:58,440
this song

329
00:14:55,100 --> 00:14:59,639
so not Eye of the Tiger but the actual

330
00:14:58,440 --> 00:15:01,860
theme

331
00:14:59,639 --> 00:15:04,920
has a name I forgot to write it down uh

332
00:15:01,860 --> 00:15:07,699
and this one uh it was a meme early in

333
00:15:04,920 --> 00:15:07,699
the 2000s

334
00:15:08,399 --> 00:15:15,620
uh my children said Mom that has lyrics

335
00:15:10,920 --> 00:15:15,620
but four lyrics don't count as lyrics

336
00:15:16,580 --> 00:15:21,420
[Music]

337
00:15:18,380 --> 00:15:24,500
okay we have the Harlem Shake and this

338
00:15:21,420 --> 00:15:27,980
one playing to my audience

339
00:15:24,500 --> 00:15:27,980
yes here we go

340
00:15:29,480 --> 00:15:34,199
Cantina Band from Star Wars so those

341
00:15:32,639 --> 00:15:37,680
were all they're about

342
00:15:34,199 --> 00:15:39,839
um uh 11 songs that are non-english

343
00:15:37,680 --> 00:15:41,519
language and there are about eight that

344
00:15:39,839 --> 00:15:44,880
are instrumentals

345
00:15:41,519 --> 00:15:47,279
um and so they were gone killed Dead uh

346
00:15:44,880 --> 00:15:50,040
okay so I needed a process in order to

347
00:15:47,279 --> 00:15:52,680
analyze what was left I have a lot of

348
00:15:50,040 --> 00:15:56,160
data and I need to work out how to how

349
00:15:52,680 --> 00:15:58,440
to determine what silly is what good is

350
00:15:56,160 --> 00:16:01,019
how do I tell my children that my

351
00:15:58,440 --> 00:16:04,380
decades were the better and so I

352
00:16:01,019 --> 00:16:07,260
invented the Jabberwocky procedure

353
00:16:04,380 --> 00:16:09,959
named because I was going to take a

354
00:16:07,260 --> 00:16:11,940
two-prong attack one was reading ease

355
00:16:09,959 --> 00:16:14,399
reading ease is a thing that teachers

356
00:16:11,940 --> 00:16:17,160
use to classify books

357
00:16:14,399 --> 00:16:20,940
um and it is an algorithm that shows you

358
00:16:17,160 --> 00:16:24,199
the kind of reading age of the text and

359
00:16:20,940 --> 00:16:27,180
this is a book for children and and

360
00:16:24,199 --> 00:16:29,940
nonsense because Jabberwocky is a poem

361
00:16:27,180 --> 00:16:31,019
that is predominantly nonsense uh so

362
00:16:29,940 --> 00:16:31,860
those are the two things I'm going to

363
00:16:31,019 --> 00:16:35,699
look at

364
00:16:31,860 --> 00:16:39,540
the first one was nonsense so over the

365
00:16:35,699 --> 00:16:41,759
decades these are the number of song a

366
00:16:39,540 --> 00:16:43,860
number of nonsense words in the Whole

367
00:16:41,759 --> 00:16:45,899
Decade in all of the lyrics of number

368
00:16:43,860 --> 00:16:48,060
ones

369
00:16:45,899 --> 00:16:49,800
um I had to chop out 1950s because the

370
00:16:48,060 --> 00:16:52,680
number was so low but the number's so

371
00:16:49,800 --> 00:16:55,740
low because 1958 and 1959 don't qualify

372
00:16:52,680 --> 00:16:58,019
as a decade uh so from the 60s then it

373
00:16:55,740 --> 00:17:00,420
goes up a bit for the 70s back down for

374
00:16:58,019 --> 00:17:03,360
the 80s and 90s and then smashes through

375
00:17:00,420 --> 00:17:05,400
the roof in the 2000s and then back down

376
00:17:03,360 --> 00:17:07,319
a little bit for 2010.

377
00:17:05,400 --> 00:17:12,780
and uh if you want to see what that

378
00:17:07,319 --> 00:17:15,179
looks like in the 2000s we have this so

379
00:17:12,780 --> 00:17:18,020
this is across all the lyrics we have

380
00:17:15,179 --> 00:17:22,740
some swear words which I have censored

381
00:17:18,020 --> 00:17:24,020
and we also have a real dislike for the

382
00:17:22,740 --> 00:17:27,179
letter G

383
00:17:24,020 --> 00:17:29,820
every word that ended in ing got turned

384
00:17:27,179 --> 00:17:32,460
into a word that ended in i n and that's

385
00:17:29,820 --> 00:17:33,780
kind of how we got to nonsense so this

386
00:17:32,460 --> 00:17:37,380
is running

387
00:17:33,780 --> 00:17:39,480
all of the lyrics against words.text and

388
00:17:37,380 --> 00:17:40,860
pulling out proper nouns so uh names

389
00:17:39,480 --> 00:17:42,900
names were pulled out because they're

390
00:17:40,860 --> 00:17:45,720
not nonsense but these are all the words

391
00:17:42,900 --> 00:17:49,740
that don't fit either into words dot

392
00:17:45,720 --> 00:17:52,740
text as a dictionary or uh is not a

393
00:17:49,740 --> 00:17:55,020
proper noun or identifiable as a proper

394
00:17:52,740 --> 00:17:58,760
noun and so that's what we had left and

395
00:17:55,020 --> 00:18:02,160
there are tons in 2000s

396
00:17:58,760 --> 00:18:03,960
so now oh I just have to make a side

397
00:18:02,160 --> 00:18:06,720
note that that my husband claims that

398
00:18:03,960 --> 00:18:09,780
word clouds are not data analysis and he

399
00:18:06,720 --> 00:18:12,179
works in data analysis so he's allowed

400
00:18:09,780 --> 00:18:13,919
to mock me for that but also across a

401
00:18:12,179 --> 00:18:17,640
Whole Decade I claim that the lyrics

402
00:18:13,919 --> 00:18:20,160
like grouping together into sizes does

403
00:18:17,640 --> 00:18:22,590
give you some information

404
00:18:20,160 --> 00:18:23,280
so now I have I'm down to

405
00:18:22,590 --> 00:18:25,280
[Music]

406
00:18:23,280 --> 00:18:25,280
um

407
00:18:26,960 --> 00:18:33,720
1069 songs

408
00:18:29,520 --> 00:18:36,660
um and that's a lot of lyrics and I have

409
00:18:33,720 --> 00:18:38,720
tidied them all up and now might be a

410
00:18:36,660 --> 00:18:41,880
bit on the thin side

411
00:18:38,720 --> 00:18:43,620
1069 I like I went with number ones but

412
00:18:41,880 --> 00:18:46,020
maybe at this stage I'm realizing maybe

413
00:18:43,620 --> 00:18:48,419
I should have gone with top tens or top

414
00:18:46,020 --> 00:18:50,220
100s but actually it's taken me a really

415
00:18:48,419 --> 00:18:52,620
long time to get to this point so I'm

416
00:18:50,220 --> 00:18:55,320
throwing it open as an open question

417
00:18:52,620 --> 00:18:57,900
please extend my research I'm not even

418
00:18:55,320 --> 00:19:01,559
sure it qualifies as research but it was

419
00:18:57,900 --> 00:19:03,960
fun and so I'm going to take those 1069

420
00:19:01,559 --> 00:19:07,380
songs and I'm going to apply the reading

421
00:19:03,960 --> 00:19:10,919
ease calculation to them

422
00:19:07,380 --> 00:19:13,340
all right so what is reading ease

423
00:19:10,919 --> 00:19:15,960
uh well there was a dude called flesh

424
00:19:13,340 --> 00:19:18,900
and you might have heard of the flesh

425
00:19:15,960 --> 00:19:21,120
Kincaid formula uh the flesh Kincaid

426
00:19:18,900 --> 00:19:23,760
formula is just taking the flesh formula

427
00:19:21,120 --> 00:19:27,000
and then turning it into like grade

428
00:19:23,760 --> 00:19:29,400
levels for books so if you are reading a

429
00:19:27,000 --> 00:19:32,220
grade level four book then it will be

430
00:19:29,400 --> 00:19:36,000
graded by the flesh Kincaid formula and

431
00:19:32,220 --> 00:19:39,120
so this is the flesh formula and he's

432
00:19:36,000 --> 00:19:42,260
basically read a whole lot of material

433
00:19:39,120 --> 00:19:47,820
and then tried to to pitch it between

434
00:19:42,260 --> 00:19:51,179
100 as being the readable and zero as

435
00:19:47,820 --> 00:19:52,860
being unreadable like very hard and so

436
00:19:51,179 --> 00:19:54,780
that's where those literals come from

437
00:19:52,860 --> 00:19:58,260
but

438
00:19:54,780 --> 00:20:00,059
you can see that the relationship with

439
00:19:58,260 --> 00:20:04,260
my is my thing going to reach yes the

440
00:20:00,059 --> 00:20:07,559
relationship is total words over total

441
00:20:04,260 --> 00:20:11,039
sentences so words per sentence and over

442
00:20:07,559 --> 00:20:13,200
here total no my thing's not

443
00:20:11,039 --> 00:20:16,320
it's there somewhere there we go total

444
00:20:13,200 --> 00:20:17,940
syllables per total words so syllables

445
00:20:16,320 --> 00:20:20,760
per word so these are the two things

446
00:20:17,940 --> 00:20:22,980
that impact the readability of any piece

447
00:20:20,760 --> 00:20:25,919
of text the number of words per sentence

448
00:20:22,980 --> 00:20:28,679
and the number of syllables per word and

449
00:20:25,919 --> 00:20:31,140
you can see from this formula that the

450
00:20:28,679 --> 00:20:33,419
number of syllables per word has a much

451
00:20:31,140 --> 00:20:34,980
bigger impact than the number of words

452
00:20:33,419 --> 00:20:36,960
per sentence but they both have an

453
00:20:34,980 --> 00:20:39,179
impact okay

454
00:20:36,960 --> 00:20:42,360
and now I'm going to show you what that

455
00:20:39,179 --> 00:20:44,760
looks like across

456
00:20:42,360 --> 00:20:48,539
across the different

457
00:20:44,760 --> 00:20:51,960
reading ease from what flesh said was

458
00:20:48,539 --> 00:20:55,620
100 is fifth graders

459
00:20:51,960 --> 00:20:59,220
and 0 to 10 is professional or

460
00:20:55,620 --> 00:21:03,299
postgraduate level okay so I have babies

461
00:20:59,220 --> 00:21:05,460
up here and post grad down here you have

462
00:21:03,299 --> 00:21:09,419
to remember that because it does kind of

463
00:21:05,460 --> 00:21:12,059
feel a bit counter-intuitive that 100 is

464
00:21:09,419 --> 00:21:15,660
the the score on reading ease but it's

465
00:21:12,059 --> 00:21:18,419
actually for the lowest level reader and

466
00:21:15,660 --> 00:21:21,480
zero is for the highest level reader

467
00:21:18,419 --> 00:21:23,520
okay just remember that and as we as we

468
00:21:21,480 --> 00:21:26,220
look at this graph syllables per word

469
00:21:23,520 --> 00:21:27,780
are going up and words per sentence are

470
00:21:26,220 --> 00:21:30,299
going up

471
00:21:27,780 --> 00:21:31,980
okay so I've decided to look at

472
00:21:30,299 --> 00:21:36,260
sentences first

473
00:21:31,980 --> 00:21:36,260
but what is a sentence

474
00:21:36,299 --> 00:21:41,039
Collins Dictionary says it's a group of

475
00:21:38,880 --> 00:21:42,780
words usually containing a verb that

476
00:21:41,039 --> 00:21:44,820
expresses a thought in the form of a

477
00:21:42,780 --> 00:21:47,640
statement question instruction or

478
00:21:44,820 --> 00:21:50,460
exclamation and starts with a capital

479
00:21:47,640 --> 00:21:52,799
letter when written okay

480
00:21:50,460 --> 00:21:55,020
but that's a PhD

481
00:21:52,799 --> 00:21:58,080
and I don't have a PhD and I'm not

482
00:21:55,020 --> 00:22:00,780
getting a PhD between July and this

483
00:21:58,080 --> 00:22:03,179
presentation so I tried some natural

484
00:22:00,780 --> 00:22:04,980
language processing libraries to see if

485
00:22:03,179 --> 00:22:07,919
I could get them to determine what a

486
00:22:04,980 --> 00:22:10,140
sentence was in my lyrics and they ran

487
00:22:07,919 --> 00:22:11,460
really really slowly and still didn't do

488
00:22:10,140 --> 00:22:14,460
a good job

489
00:22:11,460 --> 00:22:17,220
so then I thought I will use the primary

490
00:22:14,460 --> 00:22:20,280
school definition of a sentence ends

491
00:22:17,220 --> 00:22:24,059
with a full stop or exclamation mark or

492
00:22:20,280 --> 00:22:27,000
a question mark and turns out that

493
00:22:24,059 --> 00:22:30,120
lyrics are not well punctuated so that's

494
00:22:27,000 --> 00:22:33,120
really difficult so I came up with my

495
00:22:30,120 --> 00:22:35,340
estimation for a sentence

496
00:22:33,120 --> 00:22:37,740
every two lines in a song is

497
00:22:35,340 --> 00:22:40,260
approximately a sentence and I figured

498
00:22:37,740 --> 00:22:43,380
that since I'm using it and applying it

499
00:22:40,260 --> 00:22:45,419
consistently that that'll do and also

500
00:22:43,380 --> 00:22:49,200
remember that the scent the words per

501
00:22:45,419 --> 00:22:52,260
sentence impact is much lower than the

502
00:22:49,200 --> 00:22:53,820
syllables per word so let's focus on the

503
00:22:52,260 --> 00:22:55,520
syllables

504
00:22:53,820 --> 00:22:57,900
all right so

505
00:22:55,520 --> 00:22:59,460
in school when we teach students

506
00:22:57,900 --> 00:23:01,380
syllables I'm a high school teacher I

507
00:22:59,460 --> 00:23:03,299
don't teach syllables but they the

508
00:23:01,380 --> 00:23:06,480
primary school teachers tend to teach it

509
00:23:03,299 --> 00:23:08,700
with a clapping method so we have

510
00:23:06,480 --> 00:23:12,600
syllable what we're going to do is we're

511
00:23:08,700 --> 00:23:14,960
going to clap with the emphasis so sill

512
00:23:12,600 --> 00:23:18,299
La book can we all clap together

513
00:23:14,960 --> 00:23:20,640
syllable right and that's what that

514
00:23:18,299 --> 00:23:23,220
should look like okay

515
00:23:20,640 --> 00:23:25,700
so it's really hard to teach computers

516
00:23:23,220 --> 00:23:28,679
to clap

517
00:23:25,700 --> 00:23:30,539
and I found it really I I found that I

518
00:23:28,679 --> 00:23:33,720
could not do that so I went and found

519
00:23:30,539 --> 00:23:37,260
some algorithms and this dude Michael

520
00:23:33,720 --> 00:23:40,559
Holtz sure has written an excellent blog

521
00:23:37,260 --> 00:23:42,120
post and the summary is you follow this

522
00:23:40,559 --> 00:23:45,539
algorithm you Loop through the letters

523
00:23:42,120 --> 00:23:47,400
of the word if you reach a vowel a e i o

524
00:23:45,539 --> 00:23:49,980
u or Y

525
00:23:47,400 --> 00:23:52,440
and it's not next to another vowel add a

526
00:23:49,980 --> 00:23:55,080
syllable to the count if the word ends

527
00:23:52,440 --> 00:23:57,720
with e then subtract a syllable from the

528
00:23:55,080 --> 00:24:00,179
count and if the word ends with l e then

529
00:23:57,720 --> 00:24:03,780
put the syllable back so this looks like

530
00:24:00,179 --> 00:24:06,120
this we get to the Y we add one we get

531
00:24:03,780 --> 00:24:09,059
to the a we add one we get to the E we

532
00:24:06,120 --> 00:24:11,760
add one we take one off because it ends

533
00:24:09,059 --> 00:24:14,340
in an e we add one back on because it

534
00:24:11,760 --> 00:24:16,140
ends in an l e all right so it's got to

535
00:24:14,340 --> 00:24:18,480
the three that we identified with the

536
00:24:16,140 --> 00:24:20,700
Clapping method so the algorithm seems

537
00:24:18,480 --> 00:24:21,780
okay and I did run it through a bunch of

538
00:24:20,700 --> 00:24:24,900
different words

539
00:24:21,780 --> 00:24:27,780
okay so then we get to my code

540
00:24:24,900 --> 00:24:31,140
I am getting sentences I am getting

541
00:24:27,780 --> 00:24:33,840
words I'm getting syllables and then I'm

542
00:24:31,140 --> 00:24:35,880
I'm applying that flesh formula which

543
00:24:33,840 --> 00:24:37,799
has the Wikipedia link in there to the

544
00:24:35,880 --> 00:24:41,460
flash formula page

545
00:24:37,799 --> 00:24:44,820
um and then I end up with a number

546
00:24:41,460 --> 00:24:48,240
once I've applied this formula across

547
00:24:44,820 --> 00:24:51,480
all of my pandas data frame I'm learning

548
00:24:48,240 --> 00:24:54,360
a lot about lambdas more than I ever

549
00:24:51,480 --> 00:24:57,000
thought I needed to know and I get

550
00:24:54,360 --> 00:24:59,039
this graph

551
00:24:57,000 --> 00:25:01,159
and you can see here that there are two

552
00:24:59,039 --> 00:25:04,980
things that jump out as kind of weird

553
00:25:01,159 --> 00:25:09,720
something's going on in 1958

554
00:25:04,980 --> 00:25:13,440
and something's going on in 1961. so

555
00:25:09,720 --> 00:25:14,480
let's have a look at 1958 so this is per

556
00:25:13,440 --> 00:25:18,179
year

557
00:25:14,480 --> 00:25:20,280
summarized average per year and so I'm

558
00:25:18,179 --> 00:25:22,500
going to look at 1958 these are all the

559
00:25:20,280 --> 00:25:24,900
songs that were listed in 1958 as number

560
00:25:22,500 --> 00:25:26,159
one and there's only a few of them

561
00:25:24,900 --> 00:25:28,980
because it actually didn't start till

562
00:25:26,159 --> 00:25:31,260
August 1958 but you can see they're all

563
00:25:28,980 --> 00:25:34,860
way up there in the hundreds in the 100

564
00:25:31,260 --> 00:25:39,299
area so they are actually all have a

565
00:25:34,860 --> 00:25:41,820
very like a very low reading age a high

566
00:25:39,299 --> 00:25:46,159
reading ease so that kind of what that

567
00:25:41,820 --> 00:25:51,720
checks out but what's going on in 1961

568
00:25:46,159 --> 00:25:53,760
this is going on in 1961. we have an

569
00:25:51,720 --> 00:25:58,620
outlier

570
00:25:53,760 --> 00:26:00,840
so I was interested what song in 1961

571
00:25:58,620 --> 00:26:04,200
could possibly

572
00:26:00,840 --> 00:26:09,120
be postgraduate level

573
00:26:04,200 --> 00:26:12,679
postgraduate levels reading in 1961

574
00:26:09,120 --> 00:26:12,679
and then I found out

575
00:26:13,100 --> 00:26:21,150
[Music]

576
00:26:17,920 --> 00:26:21,150
[Applause]

577
00:26:23,710 --> 00:26:26,769
[Music]

578
00:26:28,679 --> 00:26:35,760
all right so The Lion Sleeps Tonight by

579
00:26:31,799 --> 00:26:39,240
the tokens may not seem to us that it's

580
00:26:35,760 --> 00:26:42,059
a very complex song but turns out it has

581
00:26:39,240 --> 00:26:44,039
an awful lot of syllables per word

582
00:26:42,059 --> 00:26:47,460
because

583
00:26:44,039 --> 00:26:51,659
um we it is very long and a whim away

584
00:26:47,460 --> 00:26:55,700
has four syllables so it becomes our our

585
00:26:51,659 --> 00:26:58,500
most complex song in number one history

586
00:26:55,700 --> 00:27:00,120
okay so what's my outcome

587
00:26:58,500 --> 00:27:02,940
well if we look at the graph that I

588
00:27:00,120 --> 00:27:07,919
snuck past a little earlier you'll see

589
00:27:02,940 --> 00:27:12,360
that I have a very clear trend line from

590
00:27:07,919 --> 00:27:15,179
1960 to 2020 that shows that the reading

591
00:27:12,360 --> 00:27:19,200
ease is getting lower which means the

592
00:27:15,179 --> 00:27:23,039
reading difficulty is getting harder but

593
00:27:19,200 --> 00:27:25,940
at the same time I have the nonsense

594
00:27:23,039 --> 00:27:30,360
following the Jabberwocky procedure

595
00:27:25,940 --> 00:27:33,539
spikes at 2000 and 2010

596
00:27:30,360 --> 00:27:36,000
so clearly if you're looking at the the

597
00:27:33,539 --> 00:27:39,659
overlap of those two graphs then the

598
00:27:36,000 --> 00:27:42,659
1990s which happens to be my decade is

599
00:27:39,659 --> 00:27:46,340
the correct and only best decade for

600
00:27:42,659 --> 00:27:48,080
singing pop music yeah

601
00:27:46,340 --> 00:27:50,700
so

602
00:27:48,080 --> 00:27:53,039
there are questions still unanswered

603
00:27:50,700 --> 00:27:55,140
about my process but I do want to point

604
00:27:53,039 --> 00:27:57,299
out it's a little bit of a joke but

605
00:27:55,140 --> 00:28:00,480
there's actually much more keeping us

606
00:27:57,299 --> 00:28:02,760
together than keeping us apart if you

607
00:28:00,480 --> 00:28:04,620
look at all of those word clouds for all

608
00:28:02,760 --> 00:28:08,360
of the lyrics in all of the number one

609
00:28:04,620 --> 00:28:14,460
songs in the 60s 70s 80s 90s 2000s and

610
00:28:08,360 --> 00:28:16,620
2010s you see that love no baby these

611
00:28:14,460 --> 00:28:18,059
are words that we're singing a lot so I

612
00:28:16,620 --> 00:28:20,100
think that there's more that keeps us

613
00:28:18,059 --> 00:28:22,620
together than keeps us apart

614
00:28:20,100 --> 00:28:26,220
so this is my

615
00:28:22,620 --> 00:28:29,039
GitHub the whole point of this talk was

616
00:28:26,220 --> 00:28:30,900
that I had a silly idea and I went out

617
00:28:29,039 --> 00:28:33,600
and I did a whole bunch of investigation

618
00:28:30,900 --> 00:28:36,360
and I went through some pain and then I

619
00:28:33,600 --> 00:28:38,159
came up with a talk and if I a high

620
00:28:36,360 --> 00:28:40,919
school teacher from Sydney can do that

621
00:28:38,159 --> 00:28:42,520
then so can all of you

622
00:28:40,919 --> 00:28:44,490
that's it

623
00:28:42,520 --> 00:28:47,619
[Applause]

624
00:28:44,490 --> 00:28:47,619
[Music]

625
00:28:52,260 --> 00:28:57,779
can you have a mug thank you

626
00:28:56,100 --> 00:29:00,860
um any questions for Amanda we've got

627
00:28:57,779 --> 00:29:00,860
time for one or two

628
00:29:08,520 --> 00:29:12,000
thank you

629
00:29:10,020 --> 00:29:13,860
that was great

630
00:29:12,000 --> 00:29:16,860
you mean the 90s being great yeah

631
00:29:13,860 --> 00:29:16,860
obviously

632
00:29:17,400 --> 00:29:23,940
um as our resident expert on what songs

633
00:29:21,240 --> 00:29:26,820
people should sing What song should

634
00:29:23,940 --> 00:29:30,419
people sing at the karaoke birds of a

635
00:29:26,820 --> 00:29:33,000
feather in terms of in Discord

636
00:29:30,419 --> 00:29:36,000
I think we should start with something

637
00:29:33,000 --> 00:29:39,779
from the 90s clearly so maybe we're

638
00:29:36,000 --> 00:29:42,960
doing uh Blaze of Glory by Bon Jovi

639
00:29:39,779 --> 00:29:46,640
I think that's that's my expert opinion

640
00:29:42,960 --> 00:29:46,640
I don't feel stitched up at all

641
00:29:46,679 --> 00:29:52,580
I think we'll end questions there

642
00:29:49,679 --> 00:29:52,580
thank you everyone

643
00:29:54,779 --> 00:29:57,320
I think