More fundamentally, I’m unsure how anyone could be right or wrong here, because as far as I can tell there’s no thesis under discussion yet. Jockers’ article isn’t published. All we have is an R package, syuzhet, which does something I would call exploratory data analysis. And it’s hard to evaluate exploratory data analysis in the absence of a specific argument.
For instance, does syuzhet smooth plot arcs appropriately? I don’t know. Without a specific thesis we’re trying to test, how would we decide what scale of variation matters? In some novels it might be a scene-to-scene rhythm; in others it might be a long arc. Until I know what scale of variation matters for a particular question, I have no way of knowing what kind of smoothing is “too much” or “too little.”*
The same thing goes, more fundamentally, for the concepts of “plot” and “emotional valence” themselves. As Jacob Eisenstein has pointed out, these aren’t concepts that have a single agreed-upon meaning. To argue about them meaningfully, we’re going to need a particular historical or formal question we’re trying to solve.
It seems to me likely that syuzhet will usefully illuminate some aspects of plot. But I have no way of knowing which aspects until I look at a test involving groups of books that readers perceive as different in some specific way. For instance, if syuzhet reliably discriminates between books with tragic and comic endings, that would already be interesting. It’s not everything we mean by plot, but it’s one important thing.
The underlying issue here is that Matt hasn’t published his article yet. So we don’t actually have a thesis to debate. What we have is a new form of exploratory data analysis, released as an R package. Conversation about exploration can be interesting; it can teach me a lot about low-pass filters; but I don’t know how it could be wrong or right until I know what the exploration is trying to reveal.
I think this holds even for Matt’s claim that he’s identified six (or seven) fundamental plot patterns. That sounds like a thesis, but I would tend to say it’s still description of exploratory analysis — in this case a clustering process. Matt has done the clustering in a principled and careful way, but clustering is still (in my eyes) basically an exploratory method. I’m not sure how to evaluate it until I know what kind of generic or historical evidence would count as confirmation that we’re looking at a coherent “plot pattern.”
There are a range of ways to get that confirmation. Lynn Cherny has explored plot using supervised methods; if you do that, predictive accuracy gives you an easy test. But unsupervised methods can also be great, in cases where tests aren’t so easy to define; it’s just that an unsupervised method needs to be supplemented by historical or formal discussion that tells you what would count as confirmation for this method. I imagine there will be some of that in Matt’s article, when it comes out.
* [Edit March 31: After playing around with some artificial data myself, I have to acknowledge that the low-pass filter option in syuzhet can behave in unintuitive ways where extreme outliers and edges are involved. I think Annie Swafford (in blog posts) and Daniel Lepage (below) have been right to emphasize this. It could be less of an issue with real data; I had to use pretty extreme outliers to “break” the filter; it’s not actually the case that the whole shape is necessarily defined by its single highest point. But my guess is that this sort of filter would only add value if you wanted to build in a strong prior that plot fluctuates on or near a particular “wavelength.” On the other hand, Matt Jockers has alluded to unpublished evidence for that sort of prior (or at least for a particular filter setting). So, after changing my opinion a couple times, I’m still not feeling I have an answer here.]
If you haven’t read Ben’s blog post, I recommend exploring it now, because I’m going to skim lightly over some of the details of his method.
At its core, the technique is not complicated. It hinges on a transformation called principal component analysis (PCA), which allows researchers to map high-dimensional data onto a two-dimensional space, while keeping individual data points as far apart as possible. You can think of PCA as a technique that gives you a “good viewing angle” for flattening out a complex object. For instance, if you’ve got eight points at the corners of a cube, you could represent them as seen in (a), but (b) might be more legible because it spreads the points out more. It does that by squashing several different physical dimensions (length and breadth) into the x axis on the page.
Ben uses this technique to reveal the structural relationship between different parts of a plot. As I understand it, he divides television scripts into six segments of equal length, and trains a topic model on all the segments. If you produce, say, 100 topics, each segment of each show is now characterized as a point in 100-dimensional space, where each dimension measures the prominence of one particular topic.
He takes the first sixth of every show and averages them to produce a single point that represents the average topic distribution for the first-sixth of all shows. After doing that for all six segments, he has six data points that represent typical segments of narrative time. Then he uses PCA to find an abstract space where those points are well separated. When he does this, he gets an arc-like structure that tends to preserve the original narrative sequence of the segments (although the algorithm isn’t directly informed about sequence). In his most detailed visualization, he even takes this down to twelfths.
But what does this mean?
From the beginning, Ben has been pretty careful to stress that he sees the parabolic shape of this pattern as an artifact of PCA. (“I should emphasize that it’s hard to imagine any other shape coming out of the PCA algorithm with the inputs I put in.”) David Bamman confirms this, showing that PCA will turn many kinds of sequential data, even random walks, into an arc. The algorithm is also good at inferring sequence: if point 1 influences point 2, and point 2 influences point 3, etc., PCA will tend to preserve their sequential relationship in the projection. (It does this even if you take 1000 different random walks and add them up to produce a composite walk.) So if we believe that the topic distribution in each segment of each story is strongly related to the topic distributions on either side, we would expect PCA to organize the composite segments of all stories in a sequential arc.
That’s sort of cool, but also suggests that the structure we’re seeing is not unique to “plots.” On the other hand, it’s worth noting that the technique does work better on fiction (and television scripts) than on nonfiction. Or, rather, it shows us something different when you apply it to nonfiction.
Here I’ve divided 2000 volumes of nineteenth-century nonfiction into ten parts, trained 200 topics on all 20,000 segments, and then created composite data points that represent the first “tenth,” second “tenth,” and so on, for all the volumes. PCA is still, somewhat remarkably, able to organize these points in the right sequence, but you have to squint a little to call this an arc. The graph is more clearly dominated by a contrast between introductions and body text. I’ve plotted two of the most important organizing topics as vectors; they include a lot of high-level abstractions and metadiscourse, whereas most of the topics in this nonfiction model are as specific as “birds eggs young wings” (and have a much smaller influence on this graph).
Also, when I say differences are interesting, I don’t mean that the composite arc Ben saw by averaging all genres was meaningless. The fact that PCA will organize ten segments of 2000 novels into a parabola is not surprising. It would do that even with a random sequence. But in practice we’re not looking at random sequences, so PCA organizes points into a parabola by drawing on actual linguistic gradients that organize narrative time. As Ben has shown in a follow-up post, PCA is able to explain the patterns in television scripts better than it can explain random sequences.
In other words, the differences we’re seeing between beginnings, middles, and ends are real differences. And it’s interesting to see what those differences are. The x and y axes in a PCA projection don’t have simple meanings, because we’ve squashed multiple dimensions into two. But we can understand the space a little better by mapping the influence exerted by different topics.
In this visualization, for instance, topics associated with dialogue (“said am know yes”) tend to move a point up the y axis. They’re more common in the middle of a narrative.
It might also be interesting to compare the way narratives from different authors or genres project into this space.
Mary Elizabeth Braddon is a sensation novelist, and her works are strongly organized by a structure that resembles the majority of other novels in the nineteenth century (or is perhaps even more distinct than usual). A book like Lady Audley’s Secret begins with a stage-setting description of domestic space and family relationships. The middle of the book is characterized by dialogue. The tone of the diction becomes progressively more sentimental* until, in the conclusion, we back away from dialogue again to summary (but a summary that is very different from the introduction in tone).
By contrast, the novels of George Eliot are… um, perhaps it would be safest to say “not as well characterized by this model of narrative sequence.” You might be tempted to look at that tangle of lines and infer some kind of cyclic structure, but it would be a bit like reading tea leaves. I know George Eliot’s novels are interesting, but I doubt that squiggle tells me why. (It’s important to remember, for instance, that Eliot’s narrative time looks more orderly and arc-like when projected into a space defined by her own writing.)
Supervised and unsupervised models
In short, I think the method Ben has developed is interesting and worth further exploration, but I also think there are real interpretive challenges here. And the interpretive challenges are not general problems that would arise with any quantitative method: they’re specific to a quirk of this one, which is that it’s poised delicately between strategies of “supervised” and “unsupervised” modeling.
Actually, I’m not sure it’s technically accurate to call PCA a model at all; it’s almost a descriptive statistic (like the mean or standard deviation of a dataset). But the attraction of the technique is a bit like the attraction of unsupervised modeling: you turn it loose on the data and it spontaneously reveals patterns.
There’s nothing at all wrong with that, but the tricky thing here is that by focusing PCA on the temporal sequence within works, we actually give it a very strong bias toward a particular sort of pattern (a sequential arc). Which means we’re actually doing something that’s a bit more supervised than it might appear. It’s more like saying “if you assume narrative time is parabola-shaped, what would be the linguistic vectors organizing that space?”
All these approaches are interesting, and potentially valid; I just think it’s important to note that none of them are giving us an unsupervised model of plot. (Even unsupervised models do make assumptions, but I would say a topic model, for instance, is slightly more open-ended than an approach that implicitly maps sequences onto arcs.) There’s nothing wrong with assuming an arc, but there might be some advantage to doing it more explicitly. If I were going to use Ben’s insight to study plot in nineteenth-century novels, I would probably drop PCA and instead train two classifiers to recognize the “ends” and “middles” of narratives. When you do that, you get a result that is actually quite parallel to the one I got by using PCA.
But with a predictive model like a classifier, I feel a little more confident in my ability to characterize the strength of the patterns I’m seeing. In this case, for instance, the classifier that recognizes ends was about 62% accurate out of sample. The classifier that recognizes middles was about 61% accurate, and since I counted six out of ten segments of each narrative as “the middle,” that’s not a lot better than random. [Later edit: This was a hasty first pass. Some simple normalization got the classifiers up to 67% and 64%. That signal is probably strong enough for people to do more interesting things with it.]
However, I want to be clear: I don’t think there’s anything wrong with using PCA for this, as long as we realize that it’s surprisingly good at inferring sequence from random walks in high-dimensional space. If plots are “arcs” (as critics have tended to assume), why not make use of that insight to analyze and visualize them? Ben’s post shows us one way to do that. Another thing I take away from this exploration is how amazing Twitter can be, because I couldn’t have fully understood what was going on here without contributions from a lot of different people.
* Re: “the tone of the diction becomes progressively more sentimental:” Matt Wilkens points out that the vectors that characterize endings here have a lot in common with the language that Sara Steger identified as characteristic of 19c sentimental fiction.
Postscript Jan 5: Have to admit I’ve found it hard to stop exploring this method. I ran it on a fiction dataset expanded to 4,000 works, and to 1922, and patterns started to become a little more legible. For instance, when I include more of her works, George Eliot no longer looks as idiosyncratic. It’s also kind of interesting to superimpose plot arcs for three different periods. Here I’ve borrowed Ben’s idea of using PCA so to speak “out-of-sample,” since each of these periods is actually projected into a different space (defined by the other two periods).
The fact that these arcs float upward may confirm something we already knew, which is that fiction tends to move away from “summary” and toward direct presentation of “scene” as historical time passes. But I think the stability of the pattern is also significant. As Ben has shown, there’s no guarantee that you’ll get an arc if you project a dataset into a PCA space defined by a different dataset. The congruence of these three arcs may not quite prove that plot *is* an arc, but it does suggest that linguistic signals of “beginnings,” “middles,” and “ends” remained broadly similar from the early nineteenth century through the early twentieth. If we wanted to confirm that, we could make more direct comparisons, but for exploratory visualization I see how PCA is useful here.