This morning Andrew N. Jackson posted an interesting alternative to the smoothing of sentiment trajectories. Instead of smoothing the trajectories with a moving average, lowess, or, dare I say it, low-pass filter, Andrew suggests cumulative summing as a “simple but potentially powerful way of re-plotting” the sentiment data. I spent a little time exploring and thinking about his approach this morning, and I’m posting below a series of “plot plots” from five novels.
I’m not at all sure about how we could/should/would go about interpreting these cumulative sum graphs, but the lack of information loss is certainly appealing. Looking at these graphs requires something of a mind shift away from the way that I/we have been thinking about emotional trajectories in narrative. Making that shift requires reframing plot movement as an aggregation of emotional valence over time, a reframing that seems to be modeling something like the “cumulative effect on the reader” as Andrew writes, or perhaps it’s the cumulative effect on the characters? Whatever the case, it’s a fascinating idea that while not fully in line with Vonnegut’s conception of plot shape does have some resonance with Vonnegut’s notion of relativity. The cumulative shapes seen below in Portrait and Gone Girl are especially intriguing . . . to me.
 All of these plots use sentiment values extracted with the AFinn method, which is what Andrew implemented in Python. Andrew’s iPython notebook, by the way, is worth a close read; it provides a lot of detail that is not in his blog post, including some deeper thinking around the entire business of modeling narrative in this way.