Text Mining: CujoStandard
When it comes to my least favourite King novels, Cujo is third. Why? It’s disjointed, for one; a lot of the book is taken up by the foibles of the Sharp Cereal Professor and honestly I can’t bring myself to care enough about the dying art of marketing kid’s cereals in the early 1980s. Also, the Trentons are not sympathetic characters. Look, I’ve written elsewhere about how your characters don’t necessarily need to be likable. I’ve gone off at length about how needing your characters to be the reader’s best friend is just a trap that encourages an immature fanbase that will rise up and kidnap you when you decide to kill those characters off…
Wait, actually, I think that was Misery.
Text Mining: RoadworkStandard
The third Bachman novel, Roadwork, is another portrait of a seethingly angry man acting out against his grievances with society. In Rage, the protagonist dealt with his anti-social angst by taking his classroom hostage and killing two teachers. In The Long Walk, the protagonist deals with it by joining a ghastly game show that runs people down to their deaths. Roadwork is a little less kinetic than either; the protagonist here, George Dawes, simply gives into inertia and refuses to progress along with everyone else. A highway extension is slated to destroy an old suburban neighbourhood and Dawes is in charge of finding both a new house to live and a new location for the industrial laundry he works for. In an act of rebellion against the inherent unfairness of the situation, he decides to do neither. He refuses to vacate his property, and ends up getting shot and killed in a stand-off with the police.
Text Mining: FirestarterStandard
Firestarter: another classic King tale of a troubled young girl who develops strange psychic powers and uses them to literally burn people alive. Charlie and her dad are chased by a mysterious U.S. alphabet agency bent on weaponizing the intersection of science and paranormal research. Half the book is the chase; the other half is the catch, and that combination makes for some interesting results, as we’ll see.
Text Mining: The ShiningStandard
I completely skipped The Shining somehow, so we’ll circle back and do that one now.
The Shining (1977)
Stephen King’s third novel finds him cycling through doing his own take on all the classic horror bits: the avenging revenant of Carrie, updating Bram Stoker’s Dracula to the modern (in 1976) age in Salem’s Lot, and now the Haunted House – in this case, a whole haunted hotel. There’s an element of Shirley Jackson’s The Haunting Of Hill House in Salem’s Lot as well; the house that the villain Barlow moves into in the Lot is a long-time haunted house inhabited by cursed individuals. The Overlook Hotel has been the destination of rich, shady people since it’s inception and by the time full-time alcoholic/on-his-last-chance writer Jack Torrence comes around to be it’s winter caretaker, it’s charged with their energies: the awful, unspeakable emotions that were left behind and whose ghosts now bestow a strong, malevolent force of will upon the hotel.
Text Mining: The StandStandard
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: RageStandard
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 LotStandard
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 + CarrieStandard
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 MiningStandard
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.