Text Mining: Firestarter

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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.

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Text Mining: The Dead Zone

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You want to talk about an out-there outlier for what we’ve seen of Stephen King’s bibliography so far, let’s talk about The Dead Zone.

A quick run-down: John Smith suffers a head injury as a kid but comes out mostly ok. Greg Stillson is a crazy but wildly charismatic traveling salesman. Johnny becomes a teacher, falls in love, and then is driven into a coma by a car accident. When he emerges he has wild psychic powers where he can touch people and know both their secrets and their future. He endures some tabloid celebrity, solves murders, tries to keep teaching and being normal, saves some kids from dying, and then discovers that Stillson, now running for office, is going to win and eventually become President briefly before destroying the world in a nuclear holocaust. Johnny becomes a would-be assassin, dying but also revealing Stillson to be a huge coward and an electoral loser after he grabs a kid as a human shield. It’s a timely examination of the American hunger for an end to the seemingly endless corrupt two-party circus and a bit of a satire of the then-blossoming American Tabloid market.

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Text Mining: The Shining

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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.

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Text Mining: The Long Walk

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Now that we’ve established that there is a link between key scenes in the plot progress of a Stephen King novel and mapped sentiment peaks coded from the text, we can spend significantly less time on analyzing each peak to show this. This will allow us to go through books with a little less ponderous text.

The Long Walk (1979)

The Long Walk is another short Bachman novel about sexually frustrated young men. This time it’s about the contestants of a gruelling, cruel national sport instituted after America’s loss in the Second World War and the institution of military rule by “The Squads.” The backdrop is briefly described but evocative for that when it is mentioned. At any rate, the protagonist is one of 100 contestants who start the Long Walk. They have to keep walking at a certain speed or they are shot by soldiers who are driving around beside them. They get three warnings to get their speed back up, otherwise the guns ring out and down goes another contestant. It’s a pretty horrifying idea when it comes right down to it, if only for how weirdly plausible it is given the modern love of both spectacle and fascism. It’s also pretty psychologically taxing, especially once the weakest contestants die off and it becomes a game to walk your opponents into the ground.

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Text Mining: The Stand

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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 WindThe 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.

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Text Mining: Rage

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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.

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Text Mining: Salem’s Lot

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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.

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Text Mining: Intro + Carrie

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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.

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Literary Fun With Text Mining

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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.

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