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.
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.