Text Mining: The Stand

The Stand (1978)

So…it may behoove you to know that The Stand, King’s gigantic, bloated, sprawling epic, was picked by American adults in 2008 as their fifth-favourite book of all time. The Bible was #1 – this is America that was being polled, after all – but The Stand kept company with other books you may be familiar with: Gone With The Wind, The Lord Of The Rings, and the Harry Potter series. Generational touchstones, in other words. As a further fact, Generation X picked it as their #1 favourite (again, behind the Bible). That’s some big company, so an examination of this one should yield some interesting results.

Text Mining: Rage

Rage (1977)

Today we turn our attention to the first Richard Bachman book, Rage, a book that lives up to it’s name in as pure a fashion as you could imagine. If you haven’t found a copy of this yet, you might want to get on that: they aren’t making any more of them, at the behest of the author. As the events depicted in the book came into depressing vogue in the 21st Century, King feared that the portrayal of Charlie Decker would give aid and comfort to others in similarly desperate emotional situations.

It’s about a school shooter, you see.

Text Mining: Salem’s Lot

SALEM’S LOT (1975)

Alright, now that we’ve established there’s some preliminary evidence of a link between emotional sentiment peaks and the plot progress of a Stephen King novel let’s keep going so we can start to see if there are patterns and also to generate a corpus of King material that we can use for topic modeling and other fun supervised/unsupervised machine learning stuff.

So, let’s go to the Lot as it slowly turns into a vampire colony.

Text Mining: Intro + Carrie

As mentioned in my previous post I’m examining Stephen King texts through the magic of text mining, using a number of tools in the R language, but especially through Julia Silge’s tidytext package. The book Text Mining With R: A Tidy Approach by Julia Silge and David Robinson was a godsend in explaining the process of using tidy data formats to store and analyze text-as-data. I will roughly summarize the basics to give you an idea as to what’s involved but there is a great deal more that can be done than I am covering here.

Literary Fun With Text Mining

My wife is doing her PhD in political science on the topic of political interest groups and how they use social media to disseminate information and reach new audiences, and how they utilize this new(ish wow we’re old) medium to effect voting behaviour. Part of this has meant learning how to mine Twitter data and analyze it through the R programming language; in order to provide technical support and to have someone to troubleshoot coding issues, I’ve also been learning to use R to mine and analyze texts. What I’ve been concentrating on, in order to learn the language and the processes, is using it to mine and visualize data gathered from fictional texts, specifically the bibliography of Stephen King. What I want to do is to analyze plot trajectories drawn from sentiment data – quantitative measures of emotional sentiment words based on established dictionaries used for that sort of thing. Research questions on this would include things like: is there a pattern that King has for his plots, based on emotional language cues? Is this pattern, if any, different from other well-known horror writers? Furthermore, are there established “archetypal” emotional plot patterns for horror books, and do these patterns differ when you switch genres – say, to fantasy, military science fiction, paranormal romance, etc. etc. down the fracture lines of human experience.

Interstitial Burn-Boy Blues

Stuart watched the kid shake and mutter to himself in the seat across the aisle. His skin looked waxy in the dingy interior bus lights, and Stuart was sure that if he reached across and caressed the kid’s forehead with the back of his hand that skin would be near to scalding. He ran his tongue along the back of his teeth and watched the kid carefully. No one else in the general vicinity seemed to be concerned. Stuart noticed an old man dozing in the seat behind the kid, and a young couple murmuring to each other beneath a blanket in the seat ahead of him.

50 Days Of Soundcloud #14

“Beggar”

I may have skipped a day. Eh.

This one is a filler track from Temporarily Abandoned Profiles, but one that I remember fondly. Brash, aggressive, noisy, almost punk rock. Good times.

50 Days Of Soundcloud #12

“Formula Modernia”

BUY SELL BUY SLEEP

Feel free to check out some books: today’s featured titles include Disappearance, only 99 cents, which if you enjoy the action bits in books and you like apocalypse fiction you’ll enjoy; What You See Is What You Get, which manages to combine the specter of ag-gag laws with criminal trials that look more like reality TV than anything else; and 9th Street Blues, about a kid delivering cobbled-together drugs in the near future ruins of Woodward, OK (and is also the jumping-off point for my new serial novel, coming soon from ATM Publishing).

Soon To Be Featured On Dirty Little Bookers!

A rather excellent artist I know gave me some advice the last time I saw him, and it was to the effect that art announcements should only be done a week or so in advance, so people don’t have time to forget them. To that end, I’m proud to announce that the November spot on literature blog Dirty Little Booker’s “Calling All Indies” feature was won by yours truly, and I’ll be featured over there starting some time in the next week (as they work a month behind or so). So, like voting in Chicago elections, visit early and visit often: www.dirtylittlebookers.com

Rappers Wordier Than Shakespeare

The Largest Vocabulary in Hip hop.

NYC coder Matt Daniels recently teamed up with Red Bull Music Academy to do an interesting sort of data analysis on hip hop. The benchmark of English literature for vocabulary usage is typically the Bard, although Daniels also points out through this study that Herman Melville also had quite the vocabulary. What about hip hop though – specifically the rappers? They make their name entirely on their words, so it seems only natural to compare them to the aforementioned benchmarks.

Mr. Daniels took the first 35,000 words in their lyrics and sorted them for unique word usages. He then took the first 5000 words of Shakespeare’s 7 most popular plays (and the first 35,000 words of Moby Dick) and compared them.

The result? Wu-Tang Clan is, in fact, nothin’ to fuck with. There are 16 rappers identified that have bigger purported vocabularies than the Bard, and 5 of them are Clan – including the Clan itself. There are, however, only three that have bigger vocabularies than Melville: Kool Keith (no surprise considering he throws in words like “moosebumps” whenever he can), the GZA (the Genius!), and, by an extremely wide margin, Aesop Rock, whose lyrics often come across like post-modernist literature.

At the bottom? DMX. Although, as one astute commenter on /r/music pointed out, Mr. Daniels obviously didn’t count the sixteen different variations on barking that DMX pulls out.