That Sentimental Feeling

Eight months ago I began a series of blog posts about my experiments using sentiment analysis as a proxy for plot movement. At the time, I had done a fair bit of anecdotal analysis of how well the sentiments detected by a machine matched my own sense of the sentiments in a series of familiar novels. In addition to the anecdotal spot-checking, I had also hand-coded every sentence of James Joyce’s novel Portrait of the Artist as a Young Man and then compared the various machine methods to my own human coded values. The similarities (seen in figure 1) were striking.


Figure 1

Soon after my first post about this work, David Bamann hired five Mechanical Turks to code the sentiment in each scene of Shakespeare’s Romeo and Juliet. David posted his results online and then Ted Underwood compared the trajectory produced by David’s turks to the machine values produced by the Syuzhet R package I had developed. Even though David’s Turks had coded scenes and Syuzhet had coded sentences, the human and machine trajectories that resulted were very similar.  Figure 2 shows the two graphs, first from David’s blog and then from Ted’s.

Screen Shot 2015-12-20 at 1.50.53 PM

Figure 2

Before releasing the package, I was fairly confident that the machine was doing a good job of approximating what human beings would think. I hoped that others, like David and Ted, would provide further validation. Many folks posted results online and many more emailed me saying the tool was producing trajectories that matched their sense of the novels they applied it to, but no one conducted anything beyond anecdotal spot checking.  After returning to UNL in late August (I had been on leave for a year), I hired four students to code the sentiment of every sentence in six contemporary novels: All the Light We Cannot See by Anthony Doerr, The Da Vinci Code by Dan Brown, Gone Girl by Gillian Flynn, The Secret Life of Bees by Sue Monk Kidd, The Lovely Bones by Alice Sebold, and The Notebook by Nicholas Sparks.

These novels were selected to cover several major contemporary genres.  They span a period from 2003 to 2014.  None are experimental in the way that Portrait of the Artist is, but they do cover a range of styles between what we might call “low-brow” to “high-brow.”  Each sentence of each novel was sentiment coded by three human raters. The precise details of this study, including statistics about inter-rater agreement and machine-to-human agreement, are part of a larger analysis I am conducting with Aaron Dominguez.

What follows are six graphs showing moving averages of the human coded sentiment along side moving averages from two of the sentiment detection methods implemented in the Syuzhet R package.  The similarity of the shapes derived from the the human and machine data is quite striking.

light code girl bees bones notebook

Cumulative Sentiments

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.[1]

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.






[1] 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.

Requiem for a low pass filter

Ben Schmidt’s and Scott Enderle’s recent entries into the syuzhet discussion have beaten the last of the low pass filter out of me. I’m not entirely ready to concede that Fourier is useless for the larger problem, but they have convinced me that a better solution than the low pass is possible and probably warranted. What that better solution is remains an open question, but Ben has given us some things to consider.

In a nutshell, there were two essential elements to Vonnegut’s challenge that the low pass method seemed to be solving.  According to Vonnegut, this business of story shape “is an exercise in relativity” in which “it is the shape of the curves that matter and not their point of origin.”  Vonnegut imagined a system of plot in which the high and lows of good fortune and ill fortune are internally relative.  In this way, a very negative book such as Blood Meridian will have an absolute high and an absolute low that can be compared to another book that, though more positive on a whole, will also have an absolute high and an absolute low. The object of analysis is not the degree of positive or negative valence but the location of the spikes and troughs of that valence relative to the beginning and end of the book.  When conceived of in these terms, the ringing artifacts of the low pass filter seem rather trivial because the objective was not to perfectly represent the valence but to dramatize the shifts in valence.

As Ben has pointed out, however, the edges of the Fourier method present a different sort of problem; they assume that story plots are periodic, repeating signals.  The problem, as Ben puts it, is that the method “imposes an assumption that the start of [a] plot lines up with the end of a plot.”

Over the weekend, Ben and I exchanged a few emails, and I acknowledged that I had been overlooking these edge distortions in favor of a big picture perspective of the general shape.  Some amount of distortion, after all, must be tolerated if we want to produce a smooth shape.  As Israel Arroyo pointed out in a tweet, “endpoints are problematic in most smoothers and filters.”  With a simple rolling window, for example, the averaging can’t start until we are already half the distance of the window into the sequence.  Figure 1, which shows four options for smoothing Portrait of the Artist, highlights the moving average problem in blue.[1]


Figure 1

Looking only at figure one, it would be hard to argue against Fourier as a beautiful representation of the plot shape.  Figure 2 shows the same four methods applied to Dorian Gray.  Here again, the Fourier method seems to provide a fair representation.  In this case, however, we begin to see a problem forming at the end of the book.  The red lowess line is trending down while the green Fourier is reaching up in order to complete its cycle.  The beginning still looks good, and perhaps the distortion at the end can be tolerated, but it’s certainly not ideal.


Figure 2

Unfortunately, some sentiment trajectories appear to create a far more pronounced problem.  At Ben’s suggestion, I ran the same experiments with Madame Bovary.  The resulting plot is shown in figure 3.  I’ve not read Bovary in many years, so I can’t recall too many details about plot, but I do remember that it does not end well for anyone.  The shape of the green Fourier line at the end of figure 3, however, suggests some sort of uptick in positive sentiment that I suspect is not present in the text. The start of the shape, on the left, also looks problematic compared to the other smoothers.


Figure 3

With the first two figures, I think a case can be made that the Fourier line offers a fair representation of the emotional trajectory.  Making such a case for Bovary is not inconceivable if we ignore the edges, but it is clearly a stretch, and there is no denying that the lowess smoother does a better job.

In our email exchange about these different options, Ben included a graphic showing how various methods model four different books.  At least in these examples, loess (fifth row of figure 4) appears to be the top contender if we seek a representation that is both maximally smooth and maximally approximate.


Figure 4

In order to fully solve Vonnegut’s challenge, an alternative to percentage chunking is still necessary.  Longer segments in longer books will tend toward a neutral valence.  Figuring that out is work for the future.  For now, the Bovary example provides precisely the sort of validation/invalidation I was hoping to elicit by putting the package online.

RIP low-pass filter.[2]


[1] There are some more elegant ways to deal with filling in the flat edges, but keeping it simple here for illustration.

[2] I’m grateful to everyone who has engaged in this discussion, especially Annie Swafford, Daniel Lepage, Ted Underwood, Andrew Piper, David Bamman, Scott Enderle, and Ben Schmidt.  It has been a very engaging couple of weeks, and along the way I could not help but think of what this discussion might have looked like in print: it would have taken years to unfold!  Despite some emotional high and lows of its own, this has been a productive exercise and a great example of how valuable open code and the digital commons can be for progress.

My Sentiments (Exactly?)

While developing the Syuzhet package–a tool for tracking relative shifts in narrative sentiment–I spent a fair amount of time gut-checking whether the sentiment values returned by the machine methods were a good match for my own sense of the narrative sentiment.  Between 70% and 80% of the time, they were what I considered to be good sentence level matches. . . but sentences were not my primary unit of interest.

Rather, I wanted a way to assess whether the story shapes that the tool produced by tracking changes in sentiment were a good approximation of central shifts in the “emotional trajectory” of a narrative.  This emotional trajectory was something that Kurt Vonnegut had described in a lecture about the simple shapes of stories.  On a chalkboard, Vonnegut graphed stories of good fortune and ill fortune in a demonstration that he calls “an exercise in relativity.”  He was not interested in the precise high and lows in a given book, but instead with the highs and lows of the book relative to each other.

Blood Meridian and The Devil Wears Prada are two very different books. The former is way, way more negative.  What Vonnegut was interested in understanding was not whether McCarthy’s book was more wholly negative than Weisberger’s, he was interested in understanding the internal dynamics of shifting sentiment: where in a book would we find the lowest low relative to the highest high. Implied in Vonnegut’s lecture was the idea that this tracking of relative high and lows could serve as a proxy for something like “plot structure” or “syuzhet.”

This was an interesting idea, and sentiment analysis offered a possible way forward.  Unfortunately, the best work in sentiment analysis has been in very different domains.  Could sentiment analysis tools and dictionaries that were designed to assess sentiment in movie reviews also detect subtle shifts in the language of prose fiction? Could these methods handle irony, metaphor, and so forth?  Some people, especially if they looked only at the results of a few sentences, might reject the whole idea out of hand. Movie reviews and fiction, hogwash!  Instead of rejecting the idea, I sat down and human coded the sentiment of every sentence in Joyce’s Portrait of the Artist. I then developed Syuzhet so that I could apply and compare four different sentiment detection techniques to my own human codings.

This human coding business is nuanced.  Some sentences are tricky.  But it’s not the sarcasm or the irony or the metaphor that is tricky. The really hard sentences are the ones that are equal parts positive and negative sentiment. Consider this contrived example:

“I hated the way he looked at me that morning, and I was glad that he had become my friend.”

Is that a positive or negative sentence?  Given the coordinating “and” perhaps the second half is more important than the first part?  I coded sentences such as this as neutral, and thankfully these were the outliers and not the norm. Most of the time–even in a complex novel like Portrait where the style and complexity of the sentences are both evolving with the maturation of the protagonist–it was fairly easy to make a determination of positive, negative, or neutral.

It turns out that when you do this sort of close reading you learn a lot about the way that authors write/express/manipulate “sentiment.”  One thing I learned was that tricky sentences, such as the one above, are usually surrounded by other sentences that are less tricky.  In fact, in many random passages that I examined from other books, and in the entirety of Portrait, tricky sentences were usually followed or preceded by other simple sentences that would clarify the sentiment of the larger passage.  This is an important observation because at the level of an individual sentence, we know that the various machine methods are not super effective.[1]  That said, I was pretty surprised by the amount of sentence level agreement in my ad hoc test.  On a sentence by sentence basis, here is how the four methods in the package performed:[2]

Bing 84% agreement
Afinn 80% agreement
Stanford 60% agreement
NRC 50% agreement

These results surprised me.  I was shocked that the more awesome Stanford method did not outperform the others. I was so shocked, in fact, that I figured I must have done something wrong.  The Stanford sentiment tagger, for example, thinks that the following sentence from Joyces Portrait is negative.

“Once upon a time and a very good time it was there was a moocow coming down along the road and this moocow that was coming down along the road met a nicens little boy named baby tuckoo.”

It was a “very good time.” How could that be negative?  I think “a very good time” is positive and so do the other methods. The Stanford tagger also indicated that the sentence “He sang that song” is slightly negative.  All of the other methods scored it as neutral, and so did I.

I’m a huge fan of the Stanford tagger; I’ve been impressed by the way that it handles negation, but perhaps when all is said and done it is simply not well-suited to literary prose where the syntactical constructions can be far more complicated than typical utilitarian prose? I need more time to study how the Stanford tagger behaved on this problem, so I’m just going to exclude it from the rest of this report.  My hypothesis, however, is that it is far more sensitive to register/genre than the dictionary based methods.

So, as I was saying, what happens with sentiment in actual prose fiction is usually achieved over a series of sentences. That simile, that bit of irony, that negated sentence is typically followed and/or preceded by a series of more direct sentences expressing the sentiment of the passage.  For example,

“She was not ugly.  She was exceedingly beautiful.”
“I watched him with disgust. He ate like a pig.”

Prose, at least the prose that I studied in this experiment, is rarely composed of sustained irony, sustained negation, sustained metaphor, etc.  Usually authors provide us with lots of clues about the sentiment we are meant to experience, and over the course of several sentences, a paragraph, or a page, the sentiment tends to become less ambiguous.

So instead of just testing the machine methods against my human sentiments on a sentence by sentence basis, I split Joyce’s portrait into 20 equally sized chunks, and calculated the mean sentiment of each.  I then compared those means to the means of my own human coded sentiments.  These were the results:

Bing 80% agreement
Afinn 85% agreement
NRC 90% agreement

Not bad.  But of course any time we apply a chunking method like this we risk breaking the text right in the middle of a key passage.  And, as we increase the number of chunks and effectively decrease the size of each passage, the values tend to decrease. I ran the same test using 100 segments and saw this:

Bing 73% agreement
Afinn 77% agreement
NRC 58% agreement (ouch)

Figure 1 graphs how the AFinn method (with 77% agreement over 100 segments) tracked the sentiment compared to my human sentiments.


Figure 1

Next I transformed all of the sentiment vectors (machine and human) using the get_transformed_values function.  I then calculated the amount of agreement. With the low pass filter set to the default of 3, I observed the following agreement:

Bing 73% agreement
Afinn 74% agreement
NRC 86% agreement

With the low pass filter set to 5, I observed the following agreement:

Bing 87% agreement
Afinn 93% agreement
NRC 90% agreement

Figure 2 graphs how the transformed AFinn method tracked narrative changes in sentiment compared to my human sentiments.[3]


Figure 2

As I have said elsewhere, my primary reason for open-sourcing this code was so that others could plot some narratives of their own and see if the shapes track well with their human sense of the emotional trajectories.  If you do that, and you have successes or failure, I’d be very interested in hearing from you (please send me an email).

Given all of the above, I suppose my current working benchmark for human to machine accuracy is something like ~80%.  Frankly, though, I’m more interested in the big picture and whether or not the overall shapes produced by this method map well onto our human sense of a book’s emotional trajectory.  They certainly do seem to map well with my sense of Portrait of the Artist, and with many other books in my library, but what about your favorite novel?

[1] For what it is worth, the same can probably be said about us, the human beings.  Given a single sentence with no context, we could probably argue about its positive or negativeness.
[2] Each method uses a slightly different value range, so when I write of “agreement,”  I mean only that the machine method agreed with the human (me) that a given sentence was positively or negatively charged.  My rating scale consisted of three values: 1, 0, -1 (positive, neutral, negative). I did not test the extent of the positiveness or the degree of negativeness.
[3] I explored low-pass values in increments of 5 all the way to 100.  The percentages of agreement were consistently between 70 and 90.

A Ringing Endorsement of Smoothing

On March 7, Annie Swafford posted an interesting critique of the transformation method implemented in Syuzhet.  Her basic argument is that setting the low-pass filter too low may result in misleading ringing artifacts.[1]  This post takes up the issue of ringing artifacts more directly and explains how Annie’s clever method of neutralizing values actually demonstrates just how effective the Syuzhet tool is in doing what it was designed to do!   But lest we begin chasing any red herring, let me be very clear about the objectives of the software.

  1. The tool is meant to reveal the simple (and latent) shape of stories, not the complex shape of stories, not the perfect shape of stories, not the absolute shape of stories, just the simple foundational shapes.[2]  This was the challenge that Vonnegut put forth when he said “There is no reason why the simple shape of stories cannot be fed into computers.”
  2. The tool uses sentiment, as detected by four possible methods, as a proxy for “plot.”  This is in keeping with Vonnegut’s conception of “plot” as a movement between what he called “good fortune” and “ill fortune.”  The gamble Syuzhet makes is that the sentiment detection methods are both “good enough” and also may serve as a satisfying proxy for the “good” and “ill” fortune Vonnegut describes in his essay and lecture.
  3. Despite some complex mathematics, there is an interpretive dimension to this work. I suppose this is why folks call it “digital humanities” instead of physics. Syuzhet was designed to estimate and smooth the emotional highs and lows of a narrative; it was not designed to provide a perfect mapping of emotional valence. I don’t think such perfect mapping is computationally possible; if you want/need that kind of experience, then you need to read the book (some of ’em are even worth it).  I’m interested in detecting/revealing the simple shape of stories by approximating the fundamental highs and lows of emotional valence. I believe that this is what Vonnegut had in mind.
  4. Finally, when examining the shapes produced by graphing the Syuzhet values, we must remember what Vonnegut said: “This is an exercise in relativity, really. It is the shape of the curves that matters and not their origins.”  When Vonnegut speaks of the shapes, he speaks of them as “simple” shapes.

In her critique of the software, Annie expresses concern over the potential for ringing artifacts when using a Fourier transformation and a very low, low-pass filter.  She introduces an innovative method for detecting this possible ringing.  To demonstrate the efficacy of her method, she “neutralizes” one third of the sentiment values in Joyce’s Portrait of the Artist as a Young Man and then retransforms and graphs the new neutralized shape against the original foundation shape of the story.

Annie posits that if the Syuzhet tool is working as she thinks it should, then the last third of the foundational shape should change in reaction to this neutralization.  In Annie’s example, however, no significant change is observed, and she concludes that this must be due to a ringing artifact.  Figure 1 (below) is the evidence she presents on her blog.

Figure 1: last third neutralized

For what it is worth, we do see some minor differences between the blue and the orange lines, but really, these look like the same “Man in Hole” plot shapes.  Ouch, this does look like a bad ringing artifact.  But could there be another explanation?

There may, indeed, be some ringing here, but it’s not nearly so extreme as Figure 1 suggests.  An alternative conclusion is that the similarity we observe in the two lines is due to a similarity between the actual values and the neutralized values.  As it happens, the last third of the novel is already pretty neutral compared to the rest of the novel.  In fact, the mean valence for the entire last third of the novel is -0.05.  So all we have really achieved in this test is to replace a section of relatively neutral valence with another segment of totally neutral valence.

This is not, therefore, a very good book in which to test for the presence of a ringing artifacts using this particular method of neutralization.  What we see here is a case of the right result but the wrong conclusion.  Which is not to say that there is not some ringing present; I’ll get to that in a moment.  But first another experiment.

If, instead of resetting those values to zero, we set them to 3 (making Portrait end on a very happy note indeed), we get a much different shape (blue line in figure 3).  The earlier parts of the novel are now represented as comparatively less negative and the end of the novel is mucho positive.


Figure 3: Portrait with artificial positive ending

And, naturally, we can also set those values very negative and produce the graph seen in figure 4.  Oh, poor Stephen.


Figure 4: Portrait with artificial negative ending

“But wait, Jockers, you can’t deny that there is still an artificial “hump” there at the end of figure 3 and an artificial trough at the end of figure 4.”   Nope, I won’t deny it, there really can be ringing artifacts.  Let’s see if we can find some that actually matter . . .

First let’s test the beginning of the novel using the Swafford method.  We neutralize the beginning third of the novel and graph it against the original shape (figure 5).  Hmm, again it seems that the foundation shape is pretty close to the original.  Is this a ringing artifact?


Figure 5: first third neutralized

Could be, but in this case it is probably just another false ringer.  Guess what, the beginning of Joyce’s novel is also comparatively neutral.  This is why the Swafford method results in something similar when we neutralize the first third of the book.  Do note that the first third is a little bit less neutral than the last third.  This is why we see a slightly larger difference between the blue and orange lines in figure 5 compared to figure 1.

But what about the middle section?

If we set the middle third of the novel to neutral, what we get is a very different shape (and a very different novel)!  Figure 6 uses the Swafford method to remove the central crisis of the novel. This is no longer a “man in hole” story, and the resulting shape is precisely what we would expect.  Make no mistake, that hump of happiness is not a ringing artifact.  That hump in the middle is now the most sustained non-negative moment in the book.  We have replaced hell with limbo (not heaven because these are neutral values), and in comparison to the other parts of the book, limbo looks pretty good!  Keep in mind Vonnegut’s message from #4 above: “This is an exercise in relativity.”  Also keep in mind that there is some scaling going on over the y-axis; in other words, we should not get too hung up on the precise position on the y-axis at the expense of seeing the simple shape.

In the new graph, the deepest trough has now shifted to the early part of the novel, which is now the location of the greatest negative valence in the story (it’s the section where Stephen gets sick and is then beaten by father Dolan). The end of the book now looks relatively darker since we no longer have the depths of hell from the midsection for comparison, but the end third of Portrait is definitely not as negative as the beginning third and this is reflected nicely in figure 6.  (This more positive ending is also evident, by the way, in the original shape–orange line–where the hump in the last third is slightly higher than the early hump.)


Figure 6: Portrait with Swaffordized Middle

So, the Swafford method proves to be a very useful tool for testing and confirming our expectations.  If we remove the most negative section of the novel, then we should see the nadir of the simple shape shift to the next most negative section.  That is precisely what we see.  I have tested this over a series of other novels, and the effect is the same (see figure 9 below, for example).  This is a great method for validating that the tool is working as expected. Thanks Annie Swafford!

“But wait a second Jockers, what about those rascally ringing artifacts you promised.”

Yes, yes, there can indeed be ringing artifacts.  Let’s go get some. . . .

Annie follows her previous analysis with what seems like an even more extreme example.  She neutralizes everything in Joyce’s Portrait except for the middle 20 sentences of the novel.[3] When the resulting graph looks a lot like the original man-in-hole graph, she says, in essence: “Busted! there is your ringing artifact Dr. J!”  Figure 7 is the graphic from her blog.


Figure 7: Only 20 (sic) sentences of Portrait

Busted indeed!  Those positive valence humps, peaking at 25 and 75 on the x-axis are dead ringers for ringers.  We know from constructing the experiment in this manner, that everything from 0 to ~49 and everything from ~51 to 100 on the x-axis is perfectly neutral, and yet the tool, the visualization, is revealing two positive humps before and after the middle section: horrible, happy, phantom humps that do not exist in the story!

But wait. . .

With all smoothing methods some degree of imprecision is to be expected.  Remember what Vonnegut points out: this is “an exercise in relativity.”  Relatively speaking, even the extreme example in figure 7 is, in my opinion, not too bad.  Just imagine a hypothetical protagonist cruising along in a hypothetical novel such as the one Annie has written with her neutral values.  This protagonist is feeling pretty good about all that neutrality; she ain’t feeling great, but she’s better than bad.  Then she hits that negative section . . . as Vonnegut would say, “oh, God damn it.”[4]  But then things get better, or more precisely, things get comparatively better.  So, the blue line is not a great representation of the narrative, but it’s not a bad approximation either.

But look, I understand my colleague’s concern for more precision, and I don’t want it to appear that I’m taking this ringing business too lightly.  Figure 8 (below) was produced using precisely the same data that Annie used in her two-sentence example; everything is neutralized except for those two sentences from the exact middle of the novel.  This time, however,  I have used a low pass filter set at 100.  Voila!  The new shape (blue) is nothing at all like the original (orange), and the new shape also provides the deep level of detail–and lack of ringing–that some users may desire.[5]  Unfortunately, using such a high, low-pass filter does not usually produce easily interpretable graphs such as seen in figure 8.


Figure 8: Original shape with neutralized “Swafford Shape” using 100 components

In this very simple example, turning the low-pass filter up to 100 produces a graph that is very easy to read/interpret.   When we begin looking at real novels, however, a low-pass of 100 does not result in shapes that are very easy to visually interpret, and it becomes necessary to smooth them.  I think that is what visualization is all about, which is to say, simplifying the complex so that we can get the gist.  One way to simplify these emotional trajectories is to use a low, low pass filter.  Given that going low may cause more ringing, you need to decide just how low you can go.  Another option, that I demonstrated in my previous post, is to use a high value for the low pass filter (to avoid potential ringing) and then apply a lowess smoother (or your own favorite smoother) in order to reveal the “simple shape” (see figure 1 of

In a future post, I’ll explore something I mentioned to Annie in our email exchange (prior to her public critique): an ad hoc method I’ve been working on that seeks to identify an “ideal” number of components for the low pass filter.


Figure 9: Dorian Gray behaving exactly as we would expect with last third neutralized


[1] Annie does not actually explain that the low-pass filter is a user controlled parameter or that what she is actually testing is the efficacy of the default value.  Users of the tool are welcome to experiment with different values for the low pass filter as I have done here: Is that your Syuzhet Ringing.

[2] I’ve been calling these simple shapes “emotional trajectories” and “plot.” Plot is potentially controversial here, so if folks would like to argue that point, I’m sympathetic.  For the first year of this research, I never used the word “plot,” choosing instead “emotional trajectory” or “simple shape,” which is Vonnegut’s term.  I realize plot is a loaded and nuanced word, but “emotional trajectory” and “simple shape” are just not really part of our nomenclature, so plot is my default term.

[3] There is a small discrepancy between Annie’s blog and her code.  Correction: Annie writes about and includes a graph showing the middle “20” sentences, but then provides code for retaining both the middle 2 and the middle 20 sentences.  Either way the point is the same.

[4] The two negative valence sentences from the middle of Portrait are as follows: “Nay, things which are good in themselves become evil in hell. Company, elsewhere a source of comfort to the afflicted, will be there a continual torment: knowledge, so much longed for as the chief good of the intellect, will there be hated worse than ignorance: light, so much coveted by all creatures from the lord of creation down to the humblest plant in the forest, will be loathed intensely.”

[5]  Annie has written that “Syuzhet computes foundation shapes by discarding all but the lowest terms of the Fourier transform.” That is a rather misleading comment. The low-pass-filter is set to 3 by default, but it is a user tunable parameter.  I explained my reasons for choosing 3 as the default in my email exchange with Annie prior to her critique.   It is unclear to me why Annie does not to mention my explanation, so here it is from our email exchange:

“. . . The short and perhaps unsatisfying answer is that I selected 3 based on a good deal of trial and error and several attempts to employ some standard filters that seek to identify a cutoff / threshold by examining the frequencies (ideal, butterworth, and several others that I don’t remember any more).  The trouble with these, and why I selected 3 as the default, is that once you go higher than 3 the resulting plots get rather more complicated, and the goal, of course, is to do the opposite, which is to say that I seek to reduce the plot to a simple base form (along the lines of what Vonnegut is suggesting).  Three isn’t magic, but it does seem to work well at rooting out the foundational shape of the story.  Does it miss some of the subtitles, yep, but again, that is the point, in part.  The longer answer is that is that this is something I’m still experimenting with.  I have one idea that I’m working with now…”

Is that Your Syuzhet Ringing?

Over the weekend, Annie Swafford published another installment in her ongoing critique of Syuzhet, the R package that I released in early February. In her recent blog post, an interesting approach for testing the get_transformed_values function is proposed[1].

Previously Annie had noted how using the default values for the low-pass filter may result in too much information loss, to which I replied that that is the point.  (Readers hung up on this point are advised to go back and watch the Vonnegut video again.) With any kind of smoothing, there is going to be information loss.  The function is designed to allow the user to tune the low pass filter for greater or lesser degrees of noise (an important point that I shall return to in a moment).

In the new post, Annie explores the efficacy of leaving the low pass filter at its default value of 3; she demonstrates how this value appears to produce a ringing artifact.  This is something that the two of us had discussed at some length in an email correspondence prior to this blogging frenzy.  In that correspondence, I promised to explore adding a gaussian filter to the package, a filter she believes would be more appropriate. Based on her advice, I have explored that option, and will do so further, but for now I remain unconvinced that there is a problem for Gauss to solve.[2]

As I said in my previous post, I believe the true test of the method lies in assessing whether or not the shapes produced by the transformation are a good approximation of the shape of the story. But remember too, that the primary point of the transformation function is to solve the problem of length; it is hard to compare the plot shape of a long novel to a short one.  The low-pass argument is essentially a visualization and noise reduction parameter.   Users who want a closer, scene by scene or sentence by sentence representation of the sentiment data, will likely gravitate to the get_percentage_values function (and a very large number of bins) as, for example, Lincoln Mullen has done on Rpubs.[3]

The downside to that approach, of course, is that you cannot compare two sentiment arcs mathematically; you can only do so by eye.  You cannot compare them mathematically because the amount of text inside each percentage segment will be quite different if the novels are of different lengths, and that would not be a fair comparison.  The transformation function is my attempt at solving this time domain conundrum.  While I believe that it solves the problem well, I’m certainly open to other options.  If we decide that the transformation function is no good, that it produces too much ringing, etc. then we should look for a more attractive alternative.  Until an alternative is found and demonstrated, I’m not going to allow the perfect to become the enemy of the good.

But, alas, here we are once again on the question of defining what is “good” and what is “good enough.”  So let us turn now to that question and this matter of ringing artifacts.

The problem of ringing artifacts is well understood in the signal processing literature if a bit less so in the narratological literature:-)  Annie has done a fine job of explicating the nature of this problem, and I can’t help thinking that this is a very clever idea of hers.  In fact, I wrote to Annie acknowledging this and noting how I wish I had thought of it myself.

But after repeating her experiment a number of times, with greater and lesser degrees of success, I decided that this exercise is ultimately a bit of a red herring.  Among other things, there are no books with zero neutral values for an entire third, but more importantly the exercise has more to do with the setting of a particular user parameter than it does with the package.

I’d like to now offer a bit of cake and eat it too.  This most recent criticism has focused on the default values for the low-pass filter that I set for the function. There is, of course, nothing preventing adjustment of that parameter by those with a taste for adventure.  The higher the number, the greater the number of components that are retained; the more components we retain, the less ringing and the closer we get to reproducing the original signal.

So let us assume for a moment that the sentiment detection methods all work perfectly. We know as a matter of fact that they don’t work perfectly (you know, like human beings), but this matter of imprecision is something we have already covered in a previous post where I showed that the three dictionary based methods tend to agree with each other and with the more sophisticated Stanford method.  So even though we know we are not getting every sentence’s sentiment just right, let’s pretend that we are, if only for a moment.

With that assumed, let us now recall the primary rationale for the Fourier transformation: to normalize the length of the x-axis.  As it happens, we can do that normalization (the cake) and also retain a great many more components than the 3 default components (eating it).  Figure 1 shows Joyce’s Portrait of the Artist transformed using a low pass filter size of 100.

This produces a graph with a lot more noise, but we have effectively eliminated any objectionable ringing.  With the addition of a smoothing line (lowess function in R), what we see once again (ta da) is a beautiful, if rather less dramatic, example of Vonnegut’s Man in Hole!  And this is precisely the goal, to reveal the plot shape latent in the noise.  The smaller low-pass filter accentuates this effect, the higher low-pass filter provides more information: both show the same essential shape.

Figure 4: Portrait with low pass at 100

Figure 1: Portrait with low pass at 100


Figure 2: Portrait with low pass at 3


Figure 3: Portrait with low pass at 20

In the course of this research, I have hand examined the transformed shapes for several dozen novels.  The number of novels I have examined corresponds to the number that I feel I know well enough to assess (and also happen to possess in digital form).  These include such old and new favorites as:

  • Portrait of the Artist
  • Picture of Dorian Grey
  • Ulysses
  • Blood Meridian
  • Gone Girl
  • Finnegans Wake (nah, just kidding)
  • . . .
  • And many more.

As I noted in my previous post, the only way to determine the efficacy of this model is to see if it approximates reality.  We have now plotted Portrait of the Artist six ways to Sunday, and every time we have seen a version of the same man in hole shape.  I’ve read this book 20 times, I have taught this book a dozen times.  It is a man in hole plot.

In my (admittedly) anecdotal evaluations, I have continued to see convincing graphs, such as the one above (and the one below in figure 4).  I have found a few special books that don’t do very well, but that is a story you will have to wait for (spoiler alert, they are not works of satire or dark humor, but they are multi-plot novels involving parallel stories).

Still, I am open to the possibility that there is some confirmation bias possible here.  And this is why I wanted to release the package in the first place.  I had hoped that putting the code on gitHub would entice others toward innovation within the code, but the unexpected criticism has certainly been healthy too, and this conversation has certainly made me think of ways that the functions could be improved.

In retrospect, it may have been better to wait until the full paper was complete before distributing the code.  Most of the things we have covered in the last few weeks on this blog are things that get discussed in finer detail in the paper. Despite more details to come, I believe, as Dryden might say, that the last (plot) line is now sufficiently explicated.

Bonus Images:


Figure 4

In terms of basic shape, Figure 4 is remarkably similar to the more dramatized version seen in figure 5 below.  If you can’t see it, you aren’t reading enough Vonnegut.


Figure 5

[1] How’s that for some awkward passive voice? A few on Twitter have expressed some thoughts on my use of Annie’s first name in my earlier response.  Regular readers of this blog will know that I am consistent in referring to people by their full names upon first mention and by their first names thereafter.  Previous victims of my “house style” have included David Mimno, David;  Dana Mackenzie, Dana; Ben Schmidt, Ben; Franco Moretti, Franco, and Julia Flanders, Julia.  There are probably others.

[2] Anyone losing sleep over this gaussian filter business is welcome to grab the code and give it a whirl.

[3] In the essay I am writing about this work, I address a number of the nuances that I have skipped over in these blog posts.  One of the nuances I discuss is an automated process for the selection of a low-pass filter size.

Some thoughts on Annie’s thoughts . . . about Syuzhet

Annie Swafford has raised a couple of interesting points about how the syuzhet package works to estimate the emotional trajectory in a novel, a trajectory which I have suggested serves as a handy proxy for plot (in the spirit of Kurt Vonnegut).

Annie expresses some concern about the level of precision the tool provides and suggest that dictionary based methods (such as the three I include as options in syuzhet) are not reliable. She writes “Sentiment analysis based solely on word-by-word lexicon lookups is really not state-of-the-art at all.” That’s fair, I suppose, but those three lexicons are benchmarks of some importance, and they deserve to be included in the package if for no other reason than for comparison.  Frankly, I don’t think any of the current sentiment detection methods are especially reliable. The Stanford tagger has a reputation for being the main contender for the title of “best in the open source market,” but even it hovers around 80 – 83% accuracy.  My own tests have shown that performance depends a good deal on genre/register.

But Annie seems especially concerned about the three dictionary methods in the package. She writes “sentiment analysis as it is implemented in the syuzhet package does not correctly identify the sentiment of sentences.” Given that sentiment is a subtle and nuanced thing, I’m not sure that “correct” is the right word here. I’m not convinced there is a “correct” answer when it comes to this question of valence. I do agree, however, that some answers are more or less correct than others and that to be useful we need to be on the closer side. The question to address, then, is whether we are close enough, and that’s a hard one. We would probably find a good deal of human agreement when it comes to the extremes of sentiment, but there are a lot of tricky cases, grey areas where I’m not sure we would all agree.  We certainly cannot expect the tool to perform better than a person, so we need some flexibility in our definition of “correct.”

Take, for example, the sentence “I studied at Leland Stanford Junior University.” The state-of-the-art Stanford sentiment parser scores this sentence as “negative.” I think that is incorrect (you are welcome to disagree;-). The “bing” method, that I have implemented as the default in syuzhet, scores this sentence as neutral, as does the “afinn” method (also in the package). The NRC method scores it as slightly positive. So, which one is correct? We could go all Derrida on this sentence and deconstruct each word, unpack what “junior” really means. We could probably even “problematize” it! . . . But let’s not.

What Annie writes about dictionary based methods not being the most state-of-the-art is true from a technical standpoint but sophisticated methods and complexity do not necessarily correlate with results.  Annie suggest that “getting the Stanford package to work consistently would go a long way towards addressing some of these issues,” but as we saw with the sentence above, simple beat sophisticated, hands down[1].

Consider another sentence: “Syuzhet is not beautiful.” All four methods score this sentence as positive, even the Stanford tool, which tends to do a better job with negation, says “positive.”

It is easy to find opposite cases where sophisticated wins the day. Consider this more complex sentence: “He was not the sort of man that one would describe as especially handsome.” Both NRC and Afinn score this sentence as neutral, Bing scores it slightly positive and Stanford scores it slightly negative. When it comes to negation, the Stanford tool tends to perform a bit better, but not always. The very similar sentence “She was not the sort of woman that one would describe as beautiful” is scored slightly positive by all four methods.

What I have found in my testing is that these four methods usually agree with each other, not exactly but close enough. Because the Stanford parser is very computationally expensive and requires special installation, I focused the examples in the Syuzhet Package Vignette on the three dictionary based methods. All three are lightning fast by comparison, and all three have the benefit of simplicity.

But, are they good enough compared to the more sophisticated Stanford parser?

Below are two graphics showing how the methods stack up over a longer piece of text. The first image shows sentiment using percentage based segmentation as implemented in the get_percentage_values() function.


Four Methods Compared using Percentage Segmentation

The three dictionary methods appear to be a bit closer, but all four methods do create the same basic shape.  The next image shows the same data after normalization using the get_transformed_values() function.  Here the similarity is even more pronounced.


Four Methods Compared Using Transformed Values

While we could legitimately argue about the accuracy of one sentence here or one sentence there, as Annie has done, that is not the point. The point is to reveal a latent emotional trajectory that represents the general sense of the novel’s plot. In this example, all four methods make it pretty clear what that shape is: This is what Vonnegut called “Man in Hole.”

The sentence level precision that Annie wants is probably not possible, at least not right now.  While I am sympathetic to the position, I would argue that for this particular use case, it really does not matter.  The tool simply has to be good enough, not perfect.  If the overall shape mirrors our sense of the novel’s plot, then the tool is working, and this is the area where I think there is still a lot of validation work to do.  Part of the impetus for releasing the package was to allow other people to experiment and report results.  I’ve looked at a lot of graphs, but there is a limit to the number of books that I know well enough to be able to make an objective comparison between the Syuzhet graph and my close reading of the book.

This is another place where Annie raises some red flags.  Annie calls attention to these two images (below) from my earlier post and complains that the transformed graph is not a good representation of the noisy raw data.  She writes:

The full trajectory opens with a largely flat stretch and a strong negative spike around x=1100 that then rises back to be neutral by about x=1500. The foundation shape, on the other hand, opens with a rise, and in fact peaks in positivity right around where the original signal peaks in negativity. In other words, the foundation shape for the first part of the book is not merely inaccurate, but in fact exactly opposite the actual shape of the original graph.

Annie’s reading of the graphs, though, is inconsistent with the overall plot of the novel, whereas the transformed plot is perfectly consistent with the novel. What Annie calls a “strong negative spike” is the scene in which Stephen is pandied by Father Arnell.  It is an important negative moment, to be sure, but not nearly as important, or as negative, as the major dip that occurs midway through the novel–when Stephen experiences Hell. The scene with Arnell is a minor blip compared to the pages and pages of hell and the pages and pages of anguish Stephen experiences before his confession.

noisy foundation

Annie is absolutely correct in noting that there is information loss, but wrong in arguing that the graph fails to represent the novel.  The tool has done what it was designed to do: it successfully reveals the overall shape of the narrative.  The first third of the novel and the last third of the novel are considerably more positive than the middle section.  But this is not meant to say or imply that the beginning and end are without negative moments.

It is perfectly reasonable to want to see more of the page to page, or scene by scene fluctuations in sentiment, and that can be easily achieved by using the percentage segmentation method or by altering the low-pass filter size.  Changing the filter size to retain five components instead of three results in the graph below.  This new graph captures that “strong negative spike” (not so “strong” compared to hell) and reveals more of the novel’s ups and downs.  This graph also provides more detail about the end of the novel where Stephen comes down off his bird-girl high and moves toward a more sober perspective for his future.

Portrait with Five Components

Portrait with Five Components

Of course, the other reason for releasing the code is so that I can get suggestions for improvements. Annie (and a few others) have already propelled me to tweak several functions.  Annie found (and reported on her blog) some legitimate flaws in the openNLP sentence parser. When it comes to passages with dialog, the openNLP parser falls down on the job. I ran a few dialog tests (including Annie’s example) and was able to fix the great majority of the sentence parsing errors by simply stripping out the quotation marks in advance. Based on Annie’s feedback, I’ve added a “quote stripping” parameter to the get_sentences() function. It’s all freshly baked and updated on github.

But finally, I want to comment on Annie’s suggestion that

some texts use irony and dark humor for more extended periods than you [that’s me] suggest in that footnote—an assumption that can be tested by comparing human-annotated texts with the Syuzhet package.

I think that would be a great test, and I hope that Annie will consider working with me, or in parallel, to test it.  If anyone has any human annotated novels, please send them my/our way!

Things like irony, metaphor, and dark humor are the monsters under the bed that keep me up at night. Still, I would not have released this code without doing a little bit of testing:-). These monsters can indeed wreak a bit of havoc, but usually they are all shadow and no teeth. Take the colloquial expression “That’s some bad R code, man.” This sentence is supposed to mean the opposite, as in “That is a fine bit of R coding, sir.”  This is a sentence the tool is not likely to get right; but, then again, this sentence also messes up my young daughter, and it tends to confuse English language learners. I have yet to find any sustained examples of this sort of construction in typical prose fiction, and I have made a fairly careful study of the emotional outliers in my corpus.

Satire, extended satire in particular, is probably a more serious monster.  Still, I would argue that the sentiment tools performs exactly as expected; they just don’t understand what they are “reading” in the way that we do.  Then again, and this is no fabrication, I have had some (as in too many) college students over the years who haven’t understood what they were reading and thought that Swift was being serious about eating succulent little babies in his Modest Proposal (those kooky Irish)!

So, some human beings interpret the sentiment in Modest Proposal exactly as the sentiment parser does, which is to say, literally! (Check out the special bonus material at the bottom of this post for a graph of Modest Proposal.) I’d love to have a tool that could detect satire, irony, dark humor and the like, but such a tool is still a good ways off.  In the meantime, we can take comfort in incremental progress.

Special thanks to Annie Swafford for prompting a stimulating discussion.  Here is all the code necessary to repeat the experiments discussed above. . .


Swift’s classic satire presents some sentiment challenges.  There is disagreement between the Stanford method and the other three in segment four where the sentiments move in opposite directions.



[1] By the way, I’m not sure if Annie was suggesting that the Stanford parser was not working because she could not get it to work (the NAs) or because there was something wrong in the syuzhet package code. The code, as written, works just fine on the two machines I have available for testing. I’d appreciate hearing from others who are having problems; my implementation definitely qualifies as a first class hack.

The Rest of the Story

My blog on February 2, about the Syuzhet package I developed for R (now available on CRAN), generated some nice press that I was not expecting: Motherboard, then The Paris Review, and several R blogs (Revolutions, R-Bloggersinside-R) all featured the work.  The press was nice, but I was not at all prepared for the focus to be placed on the one piece of the story that I had yet to explain, namely, how I used the Syuzhet code and some unsupervised machine clustering to identify what seem to be six, or possibly seven, archetypal plot shapes.  So, here now is the rest of the story. . .

In brief: (A Plot Modeling Recipe)

  1. Apply functions available in the Syuzhet package, to generate a generalized a plot shape for every book in a corpus of 41,383 novels.[1]
  2. Employ euclidean distance to build a large distance matrix by computing the similarity between every pair of novels.
  3. Use unsupervised hierarchical clustering to group books based on the similarity of their plot shape.
  4. Examine the resulting clusters with furrowed brow and say “hmmmm.”
  5. Test several methods of cluster identification (silhouette, gap statistic, elbow).
  6. Develop ad-hoc cluster identification algorithm.
  7. Observe that there are six, or maybe seven, fundamental plot shapes.
  8. Repeat everything over and over again for 12 months while worrying a lot about observing six or seven plots.


Before I reveal the six/seven plots (scroll down if you can’t wait), it’s important to point out that what I offer here is the result of two particular methods of analysis.  If you don’t like the plot shapes that these methods reveal, then you’ll be free to take issue with the methods and try a different approach.  You could, for example,

  1. Read 41,383 novels and sketch the plots of each using Vonnegut’s chalkboard. You could then spend a few decades organizing and classifying them into some sort of taxonomy.  You could then work on clustering them into a finite set of foundational shapes.  This is more or less the method Vonnegut employed, excepting, of course, that he probably only read a few hundred stories and probably only sketched out a few dozen on his chalk board.
  2. You could use another method, such as the one that Benjamin Schmidt has proposed over at his Sapping Attention blog.


In my previous post, I explained how I developed some software (named “Suyzhet” in homage to Propp) to extract plot shapes from novels based on sentiment analysis.  In order to understand how I derive the six/seven plot archetypes, we need to understand a little bit about Euclidean distance and hierarchical clustering.  The former provides a mathematical way of computing the similarity or distance between two points in space.  When that space is two dimensional, it’s pretty easy to visualize what is going on: we plot two points on an x-y grid and then measure the distance between them.  When the space is three dimensional, it gets a bit harder, but you can still imagine measuring the distance between some point about three feet off the floor in your kitchen and some point about five feet off the floor in your living room.  Once we go beyond the third dimension things get downright tricky, and we have to rely on the mathematics of the Euclidean metric. Regardless of the dimensions, though, the fundamental idea is the same: we are measuring the distance between points and the shorter that distance the more similar the points are.  In this case the points are books, and the feature that determines their point in space is their “plot shape” as derived from Syuzhet.

Once the distances between all the points are measured, we construct a “distance matrix.”  This distance matrix is just a big spread sheet where we can look up the distance from any one point to any other point.  It might look something like Figure 1.  According to this matrix, the distance between Book 1 and Book 3 is “0.5” whereas the distance between Book 2 and Book 3 is “0.25.”

Distance Matrix

Figure 1: A Distance Matrix

Hierarchical clustering methods use this distance matrix as a foundation upon which to build a hierarchy of similarities. This hierarchy is often visualized as a dendrogram such as seen in Figure 2.

Figure 2: Dendrogram

Figure 2: Dendrogram

Figure 2 is a bit like a tree (upside down); it has branches.   At any vertical point, we can cut this tree and the result would be to separate it into two or more branches, or clusters.  For example, cutting the tree in Figure 2 at a height of 225, would result in four primary clusters.  The trick with this sort of tree cutting, is identifying an “ideal” vertical position to insert the saw.  Before I get to that, though, we need to step back for a moment to those plots created with the Syuzhet software.

The Plot Thickens

In my previous post, I showed what the plots of Joyce’s Portrait and Wilde’s Dorian Grey look like when graphed using Suyzhet.  Underneath each plot graph, is a sequence of 100 numbers from which the shape of the plot is derived.  I have collected these sequences for 41,383 novels, and when I average them, I get the “super average plot archetype” seen in Figure 3.

The Super Average Plot

Figure 3: The Super Average Plot

That is kind of interesting, but things get a lot more interesting after a bit of tree cutting. If you look at the dendrogram in Figure 2 again, you see that cutting the tree just below 250 will result in two primary clusters.  After cutting the tree at that point, it is then possible to calculate a mean shape for all the books in each cluster. The result is seen in Figure 4.

Figure 4: Two Primary Plots

Figure 4: Two Primary Plots

In homage to Vonnegut, I have titled the shape on the left “man in hole.” 46% of the books in this corpus fall into this cluster.  The remaining 54% are more similar to the plot on the right, which I have named “man on hill.”  At this point, I’d encourage you to take a quick peek Maya Eilam’s very nice visualization of Vonnegut’s archetypal plot shapes.  The plots I’ll show here are not going to look quite the same, but there will be some resonance.

Looking again at the dendrogram, you can see that the two primary clusters (MOH and MIH), can be split fairly easily into a set of four clusters.  When the tree is cut in this manner, the two plots shown in Figure 4, split into four.

Figure 5: MIH Types I and II

Figure 5: MIH Types I and II

Figure 5 shows the derivatives of the man in hole plot shape.  The man in hole plot splits into one shape (“Type I”) that looks a lot like classical tragedy and another (“Type II”) that looks more like comedy.  Whatever the case, one has a much happier ending than the other.  Figure 6 shows the derivatives of the man on hill.

Figure 6: Man on Hill Types I and II

Figure 6: Man on Hill Types I and II

Here again, one plot leads us to a happy ending and the other to a rather dark conclusion.

Cutting the tree beyond these four shapes gets trickier.  It is difficult to know where precisely to stop and cut.  Move the cut point just a little bit, and we could go from having 10 clusters to 20; it is possible, in fact, to keep moving the the cut point further and further down the tree until a point at which every book is its own cluster!  Doing that, however, would be rather silly (see “Caveats” item 1 above).  So the objective is to find an “ideal” place to cut the tree such that the resulting clusters have  the greatest amount of internal homogeneity while simultaneously being as different from each other as possible.

My solution to this problem involves iterating through a series of possible cut points and then taking two measures after each cutting.  The first is a measure of cluster homogeneity the second is a measure of cluster dissimilarity.  This process is more easily described in pseudocode:

Let K be a number of possible clusters from 2 to 50.

With each iteration, I store the resulting values so that I can compare them and identify a value of K that best fulfills the objectives described above.  In order to make this test more robust, I opted to randomly select a subset of one half of the books in the corpus (roughly 20K) and run this test over and over again (each time with a new random sample).  When I did this, I found that the method identified six as the ideal number of clusters about 90% of the time.  The other 10% of the time, it said that seven or eight was a better choice.[2]

In addition to this mathematical approach, I also employed good old subjective evaluation.  The tool suggested six or seven, but this number (six, seven) would be rather useless if the resulting shapes did not make any sense to those of us who actually read the books.  So, I looked at a lot of plots; everything from two to twenty.  After twenty, I figure there is not much point because the shapes get so similar to each other that it would be rather hard to make the case that plot 19 is really all that different from plot 20.  With six and with seven, however, there remains good deal of variation.

We saw above how MIH and MOH both split into sub types.  These I labeled as MIH Type I, MIH Type II, MOH Type I, and MOH Type II.  At the cut point that results in six plots, MIH Type I and MOH Type II stay as we saw them above in figures 5 and 6, but MIH II and MOH I both split resulting in the shapes seen in Figure 7.

Figure 7: Level Six

Figure 7: Level Six

Already we can begin to see some shape repetition.  The variant of MIH seen in the lower right, is ultimately a steeper, or more extreme, version of the basic MIH.  The other three, though, appear rather more distinct.

At level seven, MOH II splits in two resulting in the shapes shown in Figure 8. After seven, we begin to see a lot more shape repetition, and though each of these shapes is unique in terms of its precise placement on the y axis, i.e. some are more happy others more dark, the arcs are generally similar.

Obviously, there is a great deal more interpretive work to be done here.  Many of these shapes, I think, can be further classified according to their “affects” and “effects.” What, for example, is the overall impression one gets from a book that takes a character to great heights (MOH) and then plunges him/her into a pit of despair from which there is no exit (as is seen in Figure 8 left).

Figure 8: Seven Plots

Figure 8: Seven Plots

But perhaps even more interesting than any of this is the possibility for movement between scales.  Scale hopping is something I advocate in Macroanalysis.  The great power of big(ish) data is that it allows us to contextualize our small reading.  Joyce’s Portrait of the Artist (Figure 9) is a type of MIH.  What other books are MIHs?  Are they popular books?  Are they classics?  Best sellers?  Can we find another telling of the same story?  This is the work that I am doing now, moving from the large to the small and back again. Figures 10-15 (below) present six popular/well-known novels and their corresponding plot types for consideration.

[Update March 2: Annie Swafford offers an interesting critique of this work on her blog.  Her post includes some comments from me in response.]


Figure 9: Joyce’s Portrait


Figure 10

Figure 10

Figure 11

Figure 11

Figure 12

Figure 12

Figure 13

Figure 13

Figure 14

Figure 14

Figure 15

Figure 15


[1] The Suyzhet package performs a certain type of text analysis, and I’m claiming that the results of this analysis may serve as a pretty darn good proxy for plot.  That said, I’ve been working on this problem for two years, and I know some specific places where it fails.  The most spectacular example of failure was discovered by my son. He’d just finished reading one of the books in my corpus, and I showed him the plot shape from the book and asked him it it made sense. He said, “well, yes, mostly.  But this spike here is all wrong.”  It was a spike in good fortune, positive valence, at precisely the place in the novel where the villains had scored a major victory.  The positive valence was associated with a several page long section in which the bad guys were having a very good time. Readers, of course, would see this as a negative moment in the text, Suyzhet does not.  Nor does Suyzhet understand irony and dark humor and so on.  On a whole, however, Suyzhet gets it right, and that’s because most books are not sustained satire, or sustained irony.  Most books end up using emotional markers in a fairly consistent and conventional way.  Indeed, even for an experimental novel such as Joyce’s Ulysses, Suyzhet produces a plot shape that I consider to be a good match to the ebbs and flows of the text.

[2] In a longer, less blog friendly version of this research that is to appear in a collection of essays on digital literary studies, I explain the mathematics in precise detail.

Revealing Sentiment and Plot Arcs with the Syuzhet Package


This post is a followup to A Novel Method for Detecting Plot posted June 15, 2014.

For the past few years, I have been exploring the relationship between sentiment and plot shape in fiction. Earlier today I posted an R package titled “syuzhet” to github. The package is designed to extract sentiment and plot information from prose. Methods for text import, sentiment extraction, and plot arc modeling are described in the documentation and in the package vignette. What follows below is a blog-friendly version of a longer academic paper describing how I employed this package to study plot in a corpus of ~50,000 novels.



When I began the research that lead to this package, my goal was to study positive and negative emotions in literature across time, much in the same way that I had studied style and theme in Macroanalysis. Along the way, however, I discovered that fluctuations in sentiment can serve as a rather natural proxy for fluctuations in plot movement. Studying plot shifts via sentiment analysis turned out to be a far more interesting project than the simple study of sentiment, and my research got a huge boost when I stumbled upon a video of Kurt Vonnegut describing plot in precisely these terms.

After seeing the video and hearing Vonnegut’s opening challenge (“There’s no reason why the simple shapes of stories can’t be fed into computers”), I set out to develop a systematic way of extracting plot arcs from fiction. I felt this might help me to better understand and visualize how narrative is constructed. The fundamental idea, of course, was nothing new. What I was after is what the Russian formalist Vladimir Propp had defined as the narrative’s syuzhet (the organization of the narrative) as opposed to its fabula (raw elements of the story).

Syuzhet is concerned with the linear progression of narrative from beginning (first page) to the end (last page), whereas fabula is concerned with the specific events of a story, events which may or may not be related in chronological order. When we study fabula, which is what we typically do in literature courses, we mentally reconstruct the events into chronological order. We hope that this reconstruction of the fabula will help us understand the experience of the characters, the core story, etc. When we study the syuzhet, we are not so much concerned with the order of the fictional events but specifically interested in the manner in which the author presents those events to readers.

Consider the technique that radio personality Paul Harvey used in his iconic radio show “The Rest of the Story.” In each story, Harvey would hold back certain key elements until the very end of the program. The narrative would appear to have reached its conclusion, and then Harvey would say, “and now, the rest of the story.” At this point, he would reveal the held back information and the listener would reconstruct the entire fabula. The effect (and affect) of Harvey’s technique, the syuzhet, was usually stunning and pleasantly surprising. Had the story been told in simple chronological order, it would have been bland, perhaps even boring. What gave Harvey’s show power was his narrative technique.

This power was largely derived from the organization of the narrative elements and the manner in which Harvey offered listeners clues and then used narrative and language to evoke both curiosity and emotional response. What Harvey said and how he said it, were critical elements to the overall effect of the story. Harvey’s success was in finding and mastering a particular style of plot, a plot that has much in common with those found in mystery and detective fiction. A series of clues is presented along side a series of misdirections and the mystery is ultimately resolved in some grand reveal that defies expectations.

A Finite Number of Plots

But this Harvey method is just one among many possible plots. Countless scholars and non-scholars have pontificated about the possibility of a finite set of fundamental or archetypal plot shapes.

One of the more recent and famous/infamous of these scholars is Christopher Booker, whose 2004 book, titled The Seven Basic Plots: Why We Tell Stories, argues for a Jungian inspired understanding of plot in terms of seven basic archetypes. Booker’s work appears to be strongly influenced by prior work describing plot in terms of conflict. These core conflicts will be familiar to students of literature: such constructions were once taught to us under the headings of “man vs. man,” “man against nature,” “man vs. society,” and so on.

Other scholars have offered other numbers. William Foster-Harris has argued in favor of three basic patterns of plot The Basic Patterns of Plot (Foster-Harris. University of Oklahoma Press, 1959.); Ronald B. Tobias has argued for twenty (Tobias, Ronald B. 20 Master Plots. Cincinnati: Writer’s Digest Books, 1993.), and Georges Polti claims that there are thirty six (The Thirty-Six Dramatic Situations. trans. Lucille Ray). So the story goes.

All of these discussions about plot typically involve some discussion of a story’s central conflict. But discussions of conflict are more appropriately classified as fabula. Nevertheless, many of these same discussions also explore the flow, or trajectory, of the narrative, and these I consider to be appropriately categorized as syuzhet. Often these discussions of plot engage visualization in order to convey the “movement” of the narrative. Perhaps the best example of this is the one offered by Vonnegut.


A Significant Problem

Still, all of these explanations of plot suffer from a significant problem: a lack of data. Each of these proposed taxonomies suffers from anecdotalism. Vonnegut draws the plot of Cinderella for us on his chalk board, and we can imagine a handful of similar plot shapes. He describes another plot and names it “man in hole,” and we can imagine a few similar stories. But our imaginations are limited.

This limitation led me to think hard about the problem of how to compare, mathematically and computationally, the shape of one story to another. Assuming I could use computers and some NLP magic to extract plot shape from narrative (see A Novel Method for Detecting Plot), it would still be impossible to compare one shape to another because of the simple fact that stories are not the same length. Vonnegut solved this problem by creating an x-axis that runs from B to E, that is, from beginning to end. What Vonnegut did not solve, however, was the real computational problem of text length.

It was tempting to consider simply breaking each book into ten or one-hundred equally sized pieces and then taking measurements of the mean emotional valence in each chunk.


Unfortunately, some of the books would have much larger chunks and with larger chunks would come the possibility of more and more diverse valence markers. What happens, in fact, is that larger chunks of text tend to have a preponderance of both positive and negative valence markers. The end result is that all the means end up very close to neutral on the y-axis of emotional valence. Indeed, books as a whole tend to have a mean valence close to zero on a scale of -1 to 1. (I tested this by calculating the mean valence for 3500 novels in my nineteenth century novels corpus and then plotting the results as a histogram. The distribution showed a clustering around zero with very few books on the extremes.)

So, I needed a way to deal with length. I needed a way to compare the shapes of the stories regardless of the length of the novels. Luckily, since coming to UNL, I’ve become acquainted with a physicist who is one of the team of scientists who recently discovered the Higgs Boson at CERN. Over coffee one afternoon, this physicist, Aaron Dominguez, helped me figure out how to travel through narrative time.

A Solution

Aaron introduced me to a mathematical formula from signal processing called the Fourier transformation. The Fourier transformation provides a way of decomposing a time based signal and reconstituting it in the frequency domain. A complex signal (such as the one seen above in the first figure in this post) can be decomposed into series of symmetrical waves of varying frequencies. And one of the magical things about the Fourier equation is that these decomposed component sine waves can be added back together (summed) in order to reproduce the original wave form–this is called a backward or reverse transformation. Fourier provides a way of transforming the sentiment-based plot trajectories into an equivalent data form that is independent of the length of the trajectory from beginning to end. The frequency domain begins to solve the book length problem.

It turns out that not all of these sine waves in the frequency domain are created equal; some play a bigger role in the construction of the original signal. In signal processing, a low-pass filter can be used to remove the background “hiss” in an audio recording, and a similar approach can be used to filter out the extremes in the sentiment trajectories. When a low-pass filter is applied to the sentiment data, it’s possible to filter and thereby smooth out a great deal of the affectual noise.

The filtered data from the frequency domain can then be reconstituted back into the time domain using the reverse transformation. At the same time, the x-axis can be normalized and the foundation shape of the story revealed.


Above you can see the core shape of Joyce’s Portrait revealed using the “bing” method of the get_sentiment function in the syuzhet package. (Check the package documentation and vignette for details on the various options and methods.)

Once a book’s plot trajectory is converted into this normalized space, we no longer have the problem of comparing books of different lengths. Compare the foundation shape of Joyce’s Portrait (above) to Wilde’s Picture of Dorain Grey (below).


The models reflect the key narrative movements in both of these plots. Young Stephen reaches a low point during and just after the sermon on hell which occurs midway through the narrative. Dorian’s life takes a dark turn as the reality of the portrait becomes apparent. But the full power of these transformed plots does not sit simply in visualization. The values that inform these visualizations can now be compared. In a follow up post, I’ll discuss how I measured and compared 40,000+ plot shapes and then clustered the resulting data in order to reveal six common, perhaps archetypal, plot shapes. . .

Plot Arcs (Schmidt Style)

A few weeks ago Ben Schmidt posted a provocative blog entry titled “Typical TV episodes: visualizing topics in screen time.” It’s worth a careful read. . .

Ben began by topic modeling the closed captioning data from a series of popular TV series and then visualizing the ten most common topics over the time span of each episode. In other words, the x-axis is time, and the y-axis is a measure of topical presence. The end result is something that begins to look a bit like what we could call plot.

Ben followed this post with an even more provocative one on 12/16/14 “Fundamental plot arcs, seen through multidimensional analysis of thousands of TV and movie scripts“. This post led a number of us (Underwood, Mimno, Cherny, etc.) to question what the approach might reveal if applied to novels . . .

In my own recent work, I have been attempting to model plot movement in narrative fiction by analyzing the rise and fall of emotional valence across narrative time. It has been clear to me, however, that my method is somewhat impoverished by a lack context for the emotions I am measuring; Ben’s topic-based approach to plot structure might be just the context I’m missing, and some correlation analysis might be just the right recipe . . . as usual, Ben has given us a lot to think about—i.e. Happy Holidays!

After following the discussion on Twitter and on Ben’s blog, David Mimno wrote to me about whipping up some of these topical plot lines based on the 500 Topic model that I had built for Macroanalysis. Needless to say, I thought this was a great idea. (David and I had previously revisited my topical data for an article in Poetics.) Within a few hours, David had run the entire collection of 500 topics and produced 500 graphs showing the general behavior of each topic across all of the 3,500 texts in my corpus. You will find the output of David’s work here:

In David’s short introductory paragraph, he calls our attention to two specific topic graphs, one for the topic labeled “school” and another labeled “punishment.” You can find my graphs for these two topics here (school) and here (punishment). In referencing these two plots, David calls our attention to one topic (school) that appears prominently at the beginnings of novels in this corpus (think Bildungsroman, perhaps?) and another topic (punishment) that tends to be prominent at the end of novels (think Newgate novels or Oliver Twist, perhaps?).

Like the data from Ben, this data David has mined from my 19th century novels topic model is incredibly rich and demands deeper inspection. I’ve only begun to digest it in bits, but I do observe that a lot of topics carrying negative valence seem to rise over the course of narrative time. This makes intuitive sense if we believe that the central conflict of a novel must grow more intense as the novel progresses. The exciting thing to do ext is to move from the macro to the micro scale and look at the individual novels within this larger context. Perhaps we’ll be able to identify archetypal patterns and then observe which novels stick to the archetypes and which digress. . . what a feast!

Luckily we have a whole new year to indulge!