Luckily I am not a developer. My data needs are simple for some ad hoc analysis. All I need is the data pulled and I am ready to go. Twitter now makes this easy now for anyone to do, just go to your settings page:
And then select the ‘Download archive’ button under ‘Your Twitter Archive’ (here it is a prompt to resend mine, as I took the screenshot after):
And boom! A CSV of all your tweets is in your inbox ready for analysis. After all this talk about working with “Big Data” and trawling through large datasets, it’s nice to take a breather a work with something small and simple.
To be honest, the Radiohead I know and love and remember is that which was a rock band without a lot of ‘experimental’ tracks – a band you discovered on Big Shiny Tunes 2, or because your friends told you about it, or because it was playing in the background of a bar you were at sometime in the 90’s.
But I really do like their music, I’ve become familiar with more of it and overall it does possess a certain unique character in its entirety. Their range is so diverse and has changed so much over the years that it would be really hard not to find at least one track that someone will like. In this way they are very much like the Beatles, I suppose.
I was interested in doing some more content analysis type work and text mining in R, so I thought I’d try song lyrics and Radiohead immediately came to mind.
Normally it would be simply a matter of throwing something together in Python using Beautiful Soup as I have done previously. Unfortunately, due to the way these particular pages were coded, that proved to be a bit more difficult than expected.
Sometimes sitting down beforehand and looking at where you are getting it from, the format it is in and how to best go about getting it into the format you need will save you a lot of wasted time and frustration in the long run. Ask questions before you begin – what format is the data in now? What is the format I need/would like it to be in to do the analysis? What steps are required in order to get from one to the other (i.e. what is the data transformation or mapping process)?
Make it easy on your other developers (and the rest of the world in general) by labeling your <div> containers and other elements with ids (which are unique!!) or at least classes. Otherwise how are people going to scrape all your data and steal it for their own ends? I joke… kind of.
There is a large range of word counts, from the two truly instrumental tracks (Treefingers on Kid A and Hunting Bears on Amnesiac) to the wordier tracks (Dollars and Cents and A Wolf at the Door). Pablo Honey almost looks like it has two categories of songs – with a split around the 80 word mark.
Okay, interesting and all, but again these are small amounts of data and only so much can be drawn out as such.
Going forward we examine two calculated quantities.
Calculated Quantities – Lexical Density and ‘Lyrical Density’
In the realm of content analysis there is a measure known as lexical density which is a measure of the number of content words as a proportion of the total number of words – a value which ranges from 0 to 100. In general, the greater the lexical density of a text, the more content heavy it is and more ‘unpacking’ it takes to understand – texts with low lexical density are easier to understand.
According to Wikipedia the formula is as follows:
where Ld is the analysed text’s lexical density, NLex is the number of lexical word tokens (nouns, adjectives, verbs, adverbs) in the analysed text, and N is the number of all tokens (total number of words) in the analysed text.
Now, I am not a linguist, however it sounds like this is just the ratio of words which are not stopwords to the total number – or could at least be approximated by it. That’s what I went with in the calculations in R using the tm package (because I’m not going to write a package to calculate lexical density by myself).
On a related note, I completely made up a quantity which I am calling ‘lyrical density’ which is much easier to calculate and understand – this is just the number of lyrics per song over the track length, and is measured in words per second. An instrumental track would have lyrical density of zero, and a song with one word per second for the whole track would have a lyrical density of 1.
Interestingly, there are outlying tracks near the high end where the proportion of words to the song length is greater than 1 (Fitter Happier, A Wolf at the Door, and Faust Arp). Fitter Happier shouldn’t even really count, as it is really an instrumental track with a synthesized voice dubbed overtop. If you listen to A Wolf at the Door it is clear why the lyrical density is so high – Thom is practically rapping at points. Otherwise Kid A and The King of Limbs seem to have less quickly sung lyrics than the other albums on average.
Lexical Density + Lyrical Density
Putting it all together, we can examine the quantities for all of the Radiohead songs in one data visualization. You can examine different albums by clicking the color legend at the right, and compare multiple albums by holding CTRL and clicking more than one.
The songs are colour-coded by album. The points are plotted by lexical density along y-axis against the lyrical density along the x-axis and sized by total number of words in the song. As such, the position of the point in the plot gives an idea of the rate of lyrical content in the track – a song like I Might Be Wrong is fitting a lot less content words into a song at a slower rate than a track like A Wolf at the Door which is packed much tighter with both lyrics and meaning.
References & Resources
Where watching TV used to be an affair of browsing through 500 channels and complaining there was nothing on, now with the advent of on-demand digital services there is choice. Instead of flipping through hundreds of channels (is that a linear search or a random walk?), most of which have nothing whatsoever that interests you, now you can search for exactly the show you are looking for and watch it when you want. Without commercials.
Wait, what? That’s amazing! No wonder people are ‘cutting the cord’ and media corporations are concerned about the future of their business model.
True, you can still browse. People complain that the selection on Netflix is bad for Canada, but for 8 dollars a month, really it’s pretty good what you’re getting. And given the…. eclectic nature of the selection I sometimes find myself watching something I would never think to look for directly, or give a second chance if I just caught 5 minutes of the middle of it on cable.
Such is the case with Red Dwarf. Red Dwarf is one of those shows that gained a cult following, and, despite its many flaws, for me has a certain charm and some great moments. This despite my not being able to understand all of the jokes (or dialogue!) as it is a show from the BBC.
The point is that before Netflix, I probably wouldn’t come across something like this, and I definitely wouldn’t watch all of it, if there wasn’t that option so easily laid out.
So I watched a lot of this show and got to thinking, why not take this as an opportunity to do some more everyday analytics?
If you’re not familiar with the show or a fan, I’ll briefly summarize here so you’re not totally lost.
The series centers around Dave Lister, an underachieving chicken-soup vending machine repairman aboard the intergalactic mining ship Red Dwarf. Lister inadvertently becomes the last human being alive when being put into stasis for 3 million years by the ship’s computer, Holly, when there is a radiation leak aboard the ship. The remainder of the ship’s crew are Arnold J. Rimmer, a hologram of Lister’s now-deceased bunkmate and superior officer; The Cat, a humanoid evolved from Lister’s pet cat; Kryten, a neurotic sanitation droid; and later Kristine Kochanski, a love interest who gets brought back to life from another dimension.
Conveniently, the Red Dwarf scripts are available online, transcribed by dedicated fans of the program. This just goes to show that the series truly does have cult following, when there are fans who love the show so much as to sit and transcribe episodes just for it’s own sake! But then again, I am doing data analysis and visualization on that same show….
First we can see the prevalence of different characters within the show over the course of the series. I’ve omitted the x-axis labels as they made the chart appear cluttered, you can see them by interacting.
Lister and Rimmer, the two main characters, have the highest amount of mentions overall. Kryten appears in the eponymous S02E01 and is then later introduced as one of the core characters at the beginning of Season 3. The Cat remains fairly constant throughout the whole series as he appears or speaks mainly for comedic value. In S01E06, Rimmer makes a duplicate of himself which explains the high number of lines by his character and mentions of his name in the script. You can see he disappears after Episode 2 of Season 7 in which his character is written out, until re-appearing in Season 8 (he appears in S07E05 as there is an episode dedicated to the rest of the crew reminiscing about him).
Holly, the ship’s computer, appears consistently at the beginning of the program until disappearing with the Red Dwarf towards the beginning of Season 6. He is later reintroduced when it returns at the beginning of Season 8.
Lister wants to bring back Kochanski as a hologram in S01E03, and she also appears in S02E04, as it is a time travel episode. She is introduced as one of the core cast members in Episode 3 of Season 7 and continues to be so until the end of the series.
Ace is Rimmer’s macho alter-ego from another dimension. He appears a couple time in the series before S07E02, in which he is used as a plot device to write Rimmer out of the show for that season.
Appearance and mentions of other crew members of the Dwarf correspond to the beginning of the series and the end (Season 8) when they are reintroduced. The Captain, Hollister, appears much more frequently towards the end of the show.
Robots appear mainly as one-offs who are the focus of a single episode. The exceptions are the Scutters (Red Dwarf’s utility droids) whose appearances coincide with the parts of the show where the Dwarf exists, and simulants which are mentioned occasionally as villians / plot devices. The toaster and snarky dispensing machine also appear towards the beginning and end, with the former also having speaking parts in S04E04.
As mentioned before, the Dwarf gets destroyed towards at the end of Season 5 until being reintroduced at the beginning of Season 8. During this time, the crew live in one of the ship’s shuttlecraft, The Starbug. You can also see that the starbug is mentioned more frequently in episodes when the crew go on excursions (e.g. Season 3, Episodes 1 and 2).
One of the recurring themes of the show is how much Lister really enjoys Indian food, particularly chicken vindaloo. That and how he’d much rather just drink beer at the pub than do anything. S04E02 (spike 1) features a monster, a Chicken Vindaloo man (don’t ask), and the whole premise of S07E01 (spike 2) is Lister wanting to go back in time to get poppadoms.
Thought this would be fun. Space is a consistent theme of the show, obviously. S07E01 is a time travel episode, and the episodes with Pete (Season 8, 6-7) at the end feature a time-altering device.
And that’s an excellent point. You don’t need necessarily need to be a subject matter expert to be an analyst, but it sure helps to have some idea what you exactly you are analyzing. Also I would think that there’s a higher probability you care about what you are trying to analyze more if you know something about it.
On that note, it was enjoyable to analyze the scripts in this manner, and see something so familiar as a television show visualized as data like any other. I think the major themes and changes in the plotlines of the show were well represented in this way.
In terms of future directions, I tried looking at the correlation between terms using the findAssocs() function but got strange results, which I believe is due to the small number of documents. At a later point I’d like to do that properly, with a larger number of documents (perhaps tweets). Also this would work better if synonym replacement for the characters was handled in the original corpus, instead of ad-hoc and after the fact (see code).
Lastly, another thing I took away from all this is that cult TV shows have very, very devoted fan-bases. Probably due to its systemic bias, there is an awful lot about Red Dwarf on Wikipedia, and elsewhere on the internet.
Red Dwarf Scripts (Lady of the Cake)
Red Dwarf Scripts (Brit Skits)
Red Dwarf (Wikipedia)