An automated sentiment analysis of Keats’s “Ode on Melancholy”

Alas. My Internet connection went out suddenly last night, leaving me without the ability to complete an assignment at the last minute.* Since the submission window for the assignment has now closed on the course website, I’m posting this here, both because the results are a bit interesting and as a gesture of good faith toward a professor for whom I have a great admiration.

So I’m interested in Stanford University‘s automated natural-language processing sentiment analysis tool, called (appropriately enough) Sentiment Analysis, and I’m interested specifically in seeing what its boundaries are. So I’m going to run one of John Keats’s six great odes of 1819, the Ode on Melancholy, through it and see how well it works. My initial prediction is that Sentiment Analysis will likely have trouble of some kind and to some degree with several aspects of the text, in one way or another, and part of the intent of this experiment is to test this hypothesis and see how it plays out. These aspects of the text that I hypothesize will be problematic are:

  1. it’s a poem, whereas the other examples I’ve seen run through software of this type have all been prose.
  2. it uses archaic (and “elevated” and “poetic”) diction, whereas other examples of sentiment analysis that I’ve seen have used everyday, contemporary language.
  3. It personifies several key emotions, treating them as proper names rather than unambiguously direct descriptions of emotional states. I’m curious to find out how this will affect the processing of the text in this regard.

I picked this particular poem in part because I know it well — I’ve been known to recite it off the top of my head in front of undergraduates, and it appears on the reading list for my dissertation’s (as-yet-unwritten) prologue — and because it already has a strong relationship to “sentiment” and to related features: emotion, affect, etc. After all, it’s the Ode on Melancholy. I wanted to see what the Sentiment Analysis program makes of it. Too, I wanted to work with a short text with complex syntactic structures, and “Melancholy” certainly qualifies. Besides, it’s on my mind in particular right now because we’ve just read Cleanth Brooks on Keats’s “Ode on a Grecian Urn” for class this week. And finally — to show my hand a bit — there’s a particular resonant discordance that exists when lining a poem by John Keats up with this particular type of automated reading tool, and I’ll talk about this late in this blog post.

A few preliminary words on the poem, in combination with some initial predictions, are in order. A thumbnail reading of (the aspects of) the poem (that interest me) might go like this, if we allow for what Cleanth Brooks called “the heresy of paraphrase”: the poem analyzes a state of mind tagged “melancholy” in the nineteenth century (and earlier, for that matter). Keats’s Ode is written when the belief that melancholy is caused by an “imbalance of humours” has lost a great deal of cultural and scientific currency, and this is no longer an unquestioned assumption of then-contemporary medicine; but he writes before the terminology “melancholy” had begun to be displaced by the later diagnosis of “depression.” In a very rough sense, then, we can take “melancholy” to be Keats’s word for “depression,” provided that we attach a number of provisios: it needs to be detached from our own contemporary understandings of neurobiology, for instance, and understood, at least to some degree, as something more of a trait than a state, though there’s a lot of blurring in that distinction. In summary: we might expect the overall evaluation of the poem to result in a judgment of “negative” or “very negative” (two of the Sentiment Analysis algorithm’s five possible evaluations) if we’re just taking the title uncritically as an indication of “what the fellow is really talking about”: “It’s an ode to [if we forget that the title’s preposition is ‘on’] melancholy! What else would we expect?”

Of course, this is not a fair way to take the title at all. This is partly because the title includes the preposition “on,” not “to,” as do the titles of two more of Keats’s six great odes. The poem is not merely an ode “in praise of” melancholy; it is an ode in the broader sense of being a poem in elevated language that meditates on a topic. Though I don’t want to take the time to discuss this here in detail, it’s worth pointing out that “Ode on Melancholy” also follows the ode genre’s originary strophe-antistrope-epode form. In this form, a topic is considered from a particular point of view in the first portion (the first ten lines of “Melancholy” provide an exposition of despair and contain a series of elevated injunctions against various specific ways of committing suicide); this position is then problematized by a reply from a different viewpoint in the second portion (the second stanza of “Melancholy” consists of a series of recommendations that the speaker suggests as ways of dealing with affective description that are more productive than suicide). The final section consists of a conclusion that considers, balances, and integrates both viewpoints (the third stanza of “Melancholy” takes a position on the melancholic position showing it to be a position that enhances an appreciation of the opportunities life offers — at least, this is a very crude paraphrase that’s made possible only by stretching the earlier provisio that we’re going to allow the heresy of paraphrase, and by stretching this as far as possible). In a broad sense, then, Keats’s “Melancholy” follows a dialectical formula in which the problems of melancholy are expounded upon, then re-framed, and where this is followed a resolution that takes a more abstract viewpoint that integrates the viewpoints of both earlier stanzas into a broader “philosophical” position.

So let’s add one more hypothesis to test to the exercise’s goals:

  1. I want to see whether Sentiment Analysis can pick up on the overall progress of Keats’s discussion of melancholy as I’ve sketched it out here, and whether its machine reading resonates with and supports my human reading, or whether there might be other features of the text that I’ve missed.

And, having set up some initial conjectures, here’s the series of experiments I ran with Sentiment Analysis.

First experiment: just running the poem through

I think it’s worth looking at the poem itself quickly here:


No, no, go not to Lethe, neither twist
Wolf’s-bane, tight-rooted, for its poisonous wine;
Nor suffer thy pale forehead to be kiss’d
By nightshade, ruby grape of Proserpine;
Make not your rosary of yew-berries,
Nor let the beetle, nor the death-moth be
Your mournful Psyche, nor the downy owl
A partner in your sorrow’s mysteries;
For shade to shade will come too drowsily,
And drown the wakeful anguish of the soul.


But when the melancholy fit shall fall
Sudden from heaven like a weeping cloud,
That fosters the droop-headed flowers all,
And hides the green hill in an April shroud;
Then glut thy sorrow on a morning rose,
Or on the rainbow of the salt sand-wave,
Or on the wealth of globed peonies;
Or if thy mistress some rich anger shows,
Emprison her soft hand, and let her rave,
And feed deep, deep upon her peerless eyes.


She dwells with Beauty—Beauty that must die;
And Joy, whose hand is ever at his lips
Bidding adieu; and aching Pleasure nigh,
Turning to poison while the bee-mouth sips:
Ay, in the very temple of Delight
Veil’d Melancholy has her sovran shrine,
Though seen of none save him whose strenuous tongue
Can burst Joy’s grape against his palate fine;
His soul shall taste the sadness of her might,
And be among her cloudy trophies hung.

For this experiment, I just copied the poem from a post on my personal blog (which was in turn sourced carefully from my edition of The Complete Poems of John Keats [fill in publication details when I get home]), removed the stanza numbers, and ran it through Sentiment Analysis. A screencap of the output, with all lines expanded, is here (note: it’s 1205 x 14907 pixels and 1.5 megabytes!), and the machine-parsable version is available here (79.6 kilobytes).

What I noticed first was that Sentiment Analysis seems, indeed, to treat each line of the poem as a separate semantic unit. Keats’s individual stanzas are each only one sentence long, though each stanza is a syntactically complex sentence with multiple independent clauses, so what Sentiment Analysis is doing here is performing a semantic analysis of sentiments not on a sentence at a time, but on one line at a time. Whether this constitutes a problem depends on what we take “the point” of the exercise to be, it seems to me: there is no reason to think that the only valid unit of analysis is the sentence or the clause, and looking closely at the composition of each line is a worthwhile exercise on its own, I think. Indeed, the individual parse trees for each line are actually illuminating as analogues with those sentence-diagramming exercises that a lot of us will remember from grammar school. The dangers here, though, are two: first, uncritically taking the analysis as “saying something authoritative” about the structure of the poem (or each individual stanza) as a whole; second, failing to appreciate that what is being diagrammed are in fact individual lines and that they have been stripped of their context within other structures of meaning in the poem. Indeed, perhaps the most productive way to think about them is as an instance of what Jerome McGann refers to as “deformative readings” in Radiant Textuality. (About which more, perhaps, another time.) But what I would like to suggest at this point is that there is a value in re-encountering the individual line as a semantic unit that exists in tension with the larger-scale grammatical structures that produce the semantic meanings of the poem in larger blocks — that the exercise helps to encounter these units in a fresh way, without yielding to the hermeneutic pressure motivated by the poem’s (occasionally more, occasionally less) enjambed structures.

Also notable was the overall distribution of the “Sentiment” of individual lines: only lines 1, 10, 14, 21, 22, 23, 25, 29, and 30 have an overall “sentiment” rating at all. Lines 1, 10, 14, 23 are judged to be “negative”; lines 21, 22, 25, 29, and 30 are judged to be “positive.” No lines were judged to be “very positive” or “very negative” by the analysis. The rest of the lines — 2–9, 12–13, 15–20, 24, and 26–28 — had no “sentiment” rating assigned at all. Nor is the basis of these decisions immediately and transparently clear: some lines (2, 3, 7, 8, 9, 11, 12, 15, 17, 18, 20, 21, 27, 28) have no overall sentiment rating assigned, even though there are ratings assigned to individual words in those lines, and often these seem to exhibit a clear pattern; while other lines (14) have an overall sentiment rating that clashes with what seems to be the prevalent rating assigned to individual words in the line; and lines 22, 29, 30 have an overall sentiment rating assigned to the line, even though no words on those lines have an individual sentiment rating. In some cases these can be provisionally explained: In line 21, for instance, it seems fair for the algorithm to let the two instances of the word “Beauty” and the (somewhat ambiguously) positive “dwell” outweigh the single negative word “die” … but I think that an algorithmic explanation of how some of these other anomalies deserves explanation. (Why does the “rich” of line 18, which is the single positive word that the algorithm identifies, outweigh the single negative, “anger”? Is being rich more significant than being angry, especially given the multiple possible meanings of “rich,” which we might reasonably expect the problem to be nervous about interpreting?)

A partial list of words I’m surprised didn’t generate a sentiment weighting at all:

  • adieu (arguably, if Keats was using this word 195 years ago, it can be thought to have entered English parlance by now. And is not parting such sweet sorrow?)
  • bane (surely a negative word)
  • death-moth (if nothing else, this could be decomposed, I think; “Wolf’s” is decomposed into “Wolf” and “’s” in line 2)
  • drown (when is this ever positive?)
  • feed
  • kiss (certainly this is more commonly positive than negative, I would think)
  • nightshade
  • nor
  • poison (when is this a positive thing?)
  • poisonous
  • rose
  • sadness
  • shrine
  • shroud
  • weeping

Second experiment: collapsing sentences into single lines

For this iteration, I collapsed each sentence (i.e., stanza) into a single line and replaced what had been capital letters on the second through tenth lines of each stanza with lowercase letters. This yielded the following text, which I submitted to the algorithm:

No, no, go not to Lethe, neither twist wolf’s-bane, tight-rooted, for its poisonous wine; nor suffer thy pale forehead to be kiss’d by nightshade, ruby grape of Proserpine; make not your rosary of yew-berries, nor let the beetle, nor the death-moth be your mournful Psyche, nor the downy owl a partner in your sorrow’s mysteries; for shade to shade will come too drowsily, and drown the wakeful anguish of the soul.

But when the melancholy fit shall fall sudden from heaven like a weeping cloud, that fosters the droop-headed flowers all, and hides the green hill in an April shroud; then glut thy sorrow on a morning rose, or on the rainbow of the salt sand-wave, or on the wealth of globed peonies; or if thy mistress some rich anger shows, emprison her soft hand, and let her rave, and feed deep, deep upon her peerless eyes.

She dwells with Beauty—Beauty that must die; and Joy, whose hand is ever at his lips bidding adieu; and aching Pleasure nigh, turning to poison while the bee-mouth sips: ay, in the very temple of Delight veil’d Melancholy has her sovran shrine, though seen of none save him whose strenuous tongue can burst Joy’s grape against his palate fine; his soul shall taste the sadness of her might, and be among her cloudy trophies hung.

I should say immediately that this is already a reinterpretation of Keats’s text in some important ways: by removing line breaks, I’ve removed an important form of punctuation; and by removing line indents, I’ve discarded a set of implicit “tags” that not only (perhaps) provide oral performance notes, but have also obscured the ode’s historical relationship to the form’s — and this particular poem’s — roots in ancient Greek musical performance. Too, it now “looks like” prose, which is misleading in a number of subtle but important ways. But I’ll deform the poem more in the next experiment, so I’ll just move on and say that here is the graphical representation of the parse tree (1205 x 2167 pixels, 604 kilobytes), and here is the machine-parsable output (87.4 kilobytes).

What’s noticeable here? A number of things, actually, especially in comparison with the previous results. Perhaps most immediately apparent is the fact that the algorithm is doing a better job of parsing what I will (admittedly roughly) call the “prose-like” semantic structures of each individual sentence-stanza; it is able, for instance, to let the computed sentimental value of the words it can evaluate propagate to higher levels of the semantic tree. Immediately noticeable to me, then, is that each stanza gets an overall “sentiment” ranking: the first stanza is ranked “very negative” — this is certainly not surprising; it’s a series of injunctions to the listener to refrain from committing suicide. But this is the first time in this analysis that any semantic structure has been ranked “very negative,” suggesting that achieving this ranking requires a number of previous rankings of “negative” — or, at least, that having a number of lower-level rankings of this nature is a way to move more quickly toward this ranking for higher-order semantic structures.

There’s a lot that could be said about the individual parse trees here and how they differ from the first experiment, but I’ll confine myself to one observation, in the interest of brevity: this is simply that the larger-scale parse trees incorrectly identify a set of grammatical structures in the stanza-sentences that falsely construct a structural “movement” in the poem. In more detail: the first stanza results in a parse tree that is more or less visually balanced, because the stanza’s major grammatical structures are roughly balanced; there are two large-scale “chunks” of four and six lines that the algorithm takes (correctly, I think) to be the top-level grammatical structure of the sentence-stanza. The second stanza, however, has the same structure — four lines ending with a semicolon, then six more lines — but the increased syntactic complexity of the second group of lines produces a visual effect that makes the stanza seem to be “back-loaded” in a way that the syntactic structures themselves don’t support. This is a problem of the hermeneutics applicable to the visualization; we (well, I, anyway) expect (perhaps unfairly) that visualizations will reveal structures in a more or less transparent way; this is often why we employ them: to throw structural features into immediate relief so that we can notice features that aren’t immediately apparent. But this visualization requires more careful attention to its details than I think we expect visualizations to require.

However, processing the text in this way before running it through Sentiment Analysis reveals an interesting feature that confirms my own thumbnail sketch of a reading above: the poem’s emotional tone develops from one stanza to the next, resulting in machine readings of the stanzas as “very negative,” “negative,” and “positive,” respectively. That is to say that, despite the numerous words that aren’t processed at all, the algorithm’s hermeneutics, when the text gets some preliminary hand-holding, pick up on an important emotional feature of the poem’s structural and intellectual dialectic that took me multiple readings to theorize explicitly.

Third experiment: modernization of spelling and punctuation

In this pass, I adapted the prose adaptation from the second pass further by regularizing Keats’s spelling and punctuation to (what I take to be) contemporary American usage. There are two major assumptions that I’m making here. First, I’m hypothesizing that the adoption of modern spelling will facilitate increased automated understanding of the adapted text, because the majority of texts that the program has encountered in the past are likely to have originated in the contemporary era (and the program is gradually being trained by feedback from its users). Second, I’m hypothesizing that adapting Keats to (what I take to be) contemporary American punctuation may facilitate the processing of grammatical structures by a program (presumably) written by Americans.

I’m also de-capitalizing the personified emotions in “Melancholy” to see whether that affects the algorithm’s evaluation of their emotional tone. Capitalized “Joy” in the third stanza, for instance, has in previous experiments been taken as a neutral word. On reflection, this seems to me to be a sensible decision for a program designed to engage in processing of contemporary text: “Joy” is also a name, and when it occurs in this way, it seems that the safest assumption for the algorithm to make is that it has no emotional connotation. (We can think of Joy/Hulga in Flannery O’Connor’s “Good Country People” for a strong implicit argument for this, if a literary example is thought desirable.) But, after all, the fact that someone named “Joy” may very well be angry or depressed “in real life” isn’t really relevant to the question of personified abstractions in the same way that it is to an evaluation of O’Connor’s story: it seems clear on even brief examination that Keats’s personification “Joy” is likely to be most profitably taken as, well, a personification of that emotion, and therefore to have emotional content. I want to see if de-capitalizing the noun has that effect.

Here is the text that I submitted for processing this time:

No, no, go not to Lethe, nor twist wolf’s bane, tight-rooted, for its poisonous wine; nor suffer your pale forehead to be kissed by nightshade, ruby grape of Proserpine. Make not your rosary of yew-berries, nor let the beetle, nor the death-moth be your mournful Psyche, nor the downy owl a partner in your sorrow’s mysteries. For shade to shade will come too drowsily, and drown the wakeful anguish of the soul.

But when the melancholy fit shall fall, sudden from heaven like a weeping cloud that fosters the droop-headed flowers all, and hides the green hill in an April shroud, then glut your sorrow on a morning rose, or on the rainbow of the salt sand-wave, or on the wealth of globed peonies; or if your mistress some rich anger shows, imprison her soft hand, and let her rave, and feed deep, deep upon her peerless eyes.

She dwells with beauty—beauty that must die, and joy, whose hand is ever at his lips, bidding adieu, and aching Pleasure nigh, turning to poison while the bee mouth sips: yes, in the very temple of delight, veiled melancholy has her sovereign shrine, though seen of none save him whose strenuous tongue can burst joy’s grape against his palate fine. His soul shall taste the sadness of her might, and be among her cloudy trophies hung.

This is admittedly a much more radical reinterpretation of Keats’s poem than the previous alteration, and yet I’ve tried to take a middle-of-the-road approach in the kinds of interpretive choices I’ve made: I’ve broken up sentences occasionally where (I feel that) contemporary American usage would find Keats’s structure very unweildy, yet I haven’t moved adjectives before the nouns they modify. There are several other problematic moves that I’ve made that I’ll pass over in silence here, as well. A more radical contemporary American “prose translation” would be interesting to scan with Sentiment Analysis, but I’m going to skip it. At least for now.

Here‘s the graphically rendered parse trees (which, noticeably, result in a new tree for each sentence, not each paragraph). And here is the machine-parsable output.

I find one thing primarily notable here besides the fact that the algorithm is processing individual sentences, not paragraphs: de-capitalizing nouns does, in fact, affect their machine-perceived emotional content. Lowercase-D “delight” is perceived (appropriately, I think) as “very positive,” as is “joy” when it appears with an initial lowercase J.

Unconducted experiments

As I’ve run out of time to play with this particular tool tonight, I’d like to suggest briefly that a few things that I don’t have time to play with would be interesting experiments to run:

  • Removing punctuation entirely and seeing how the parsed tree structure looks, as well as what the overall evaluation of the tone is.
  • A series of transformations of the poem in which vocabularies and sentence structures are adapted to contemporary American prose usage in various ways:
    • Simply moving adjectives before the nouns they modify.
    • Using various vocabulary substitutions that approximate more and more closely Basic and/or Simplified English.
      • A humorous take on how this might play out might be inferred from xkcd comic #1133, “Up Goer Five.”
  • A set of experiments to see how the algorithm does when I’ve specifically trained it on how I think various non-evaluated words should be read. This would give additional insight into the workings of the algorithm itself … and would, arguably, be a small public-service contribution toward improving the algorithm itself. I’ve avoided doing this tonight for two reasons:
    1. I’m primarily interested — at least, this week, on my first encounter with the algorithm — in seeing how it does on its own. I think of this is a rather radical approach, especially in the first experiment, when no human assistance aside from typing was provided: what emotional structures can the algorithm detect without any more help than this?
    2. I think that the question of exactly how to rank various words, even within the rather coarse levels of granularity offered by Sentiment Analysis, deserves more thought than I have time to give it right now. Too, there is the consideration of how my rankings might influence the overall rankings of the program, which also deserves more thought than I have time to give it right now.

Ideally, of course, all of these experiments would be accompanied by thoughtful readings of what is actually happening to the performative aspects of Keats’s poem when the transformations are made. If, as Archibald MacLeish has said, “A poem should not mean / But be,” then what is the poem when these transformations are applied? This is not a simple question, and and there seems to be a fruitful opportunity here to explore the intersection of traditional close-reading techniques and more innovating machine-reading techniques.

And it’s worth saying that I wish I had more time to dig into the details of how the algorithm works, and that observing its emergent behaviors under more experiments would provide more insight into this.

Some provisional conclusions

On one hand, I think that the program does a fairly remarkable job of assessing emotional tone within its limited scope. (But perhaps this is — at least partly — because one of my experiments dovetails with some of my own preliminary hypotheses. Let us not rule out investigator bias.) The experiments, I feel, have resulted in basically successful and, in some ways, rather perceptive automated readings by the algorithm. I’m also pleased that the algorithm’s reading processes match mine in some basic ways, and tend to think that this confirms my own readings.

On the other hand, I think that there are a number of ways in which the algorithm is structured in ways that restrict its reading possibilities. The most glaringly obvious is that the algorithm “reads” only along a single axis (negative—positive) and with only five levels of granularity (very negative, negative, neutral, positive, very positive). Of course, we tend to think that sentiment is more complex than this: there is more than one axis along which it should be mapped, and there are more granular levels than the algorithm seems to appreciate.

A related (though somewhat more abstract) point is that the single-axis reduction of what might be called “the sentimental field” invokes a rather regrettable term, one that is loaded with a lot of unnecessary and harmful baggage in contemporary discourse: “negative.” I am objecting here specifically to the uncritical judgment that is associated with the knee-jerk rejection of something that gets labeled as “negativity.” Surely this label is preeminently idiotic in our own age: looking at how it is being used right now on Twitter — and looking at how quickly new results appear in that search — will almost certainly show that it is an excuse to avoid critique in favor of merely labeling and avoiding without engaging in critique. “Negativity” applies an abstracting suffix to something that is already a very abstract noun; and, it seems to me, the deployment of this term is inevitably a dismissive move that is used to shut down discourse by denying — and denying implicitly — that critique is meaningful in a discussion. But of course an honest search for truths, in whatever sense we understand that term, requires that any position be open at least in principle to critique and discussion; and so any rhetorical move that aims to close down the possibility of critique and discussion is a sin against the intellect — and a move that abstracts from actual data twice, while denying that the data needs to be interpreted, is triply a sin against the intellect.

More concretely, of course, what is shut down in the refusal to countenance even the possibility of critique is the opportunity for productive change, because productive change requires that the imperfect nature of an existing state of affairs be recognized. The possibility for this to happen depends, to a large extent, on the ability to state explicitly that the existing state of affairs is imperfect. If this potential for critique is shut down, then productive change becomes possible only serendipitously, when a sudden epiphany happens to descend from above. Or, to put it another way, shutting down critique on the basis of tone is poor critical thinking, because, as even a venture capitalist recognizes, responding to tone is a poor way to debate (look for level “DH2” in that rather short essay).

For all of these reasons, I find it regrettable that Sentiment Analysis chooses to label one of its conceptual axis “negative” — in a nutshell, I think it constitutes a failure to disentangle its automated analyses from the implied judgment that the features of language that it picks out at that end of the spectrum are “bad.” But this knee-jerk reaction that Sentiment Analysis (intentionally or otherwise) plays into has problems of its own. As Tom Scocca has put it:

Over time, it has become clear that anti-negativity is a worldview of its own, a particular mode of thinking and argument, no matter how evasively or vapidly it chooses to express itself. For a guiding principle of 21st century literary criticism, BuzzFeed’s [Isaac] Fitzgerald turned to the moral and intellectual teachings of Walt Disney, in the movie Bambi: “If you can’t say something nice, don’t say nothing at all.”

The line is uttered by Thumper, Bambi’s young bunny companion, but its attribution is more complicated than that—Thumper’s mother is making him recite a rule handed down by his father, by way of admonishing her son for unkindness. It is scolding, couched as an appeal to goodness, in the name of an absent authority.

The same maxim—minus the Disney citation and tidied up to “anything at all”—was offered by an organization called PRConsulting Group recently, in support of its announcement that the third Tuesday in October would be “Snark-Free Day.” “[I]f we can put the snark away for just one day,” the publicists wrote, “we can all be happier and more productive.” Is a world where public-relations professionals are more productive a more productive world overall? Are the goals of the public-relations profession the goals of the world in general?

(I might mention that I have a great admiration for this quite long article, and that much of what Scocca has to say is directly relevant here, and that I am quite looking forward to talking about this article with my freshman comp. students at the end of this quarter.) This is directly relevant to the question of how we read machine-produced readings, and related to the observations above about the expected hermeneutic transparency of visualizations: subcontracting the task of reading out to machines has clear benefits that Moretti has talked about [insert quote, or at least reference, here]; but, then, subcontracting the task of reading out, at all, either to machines or other humans, brings with it the associated dangers of dependence on the interpretations that those others, whoever they may be, produce. I do not mean to suggest that this means that we should jettison the possibilities of machine reading; I am merely noting that uncritical dependence on machine readings elides options for critical engagement directly with texts — and that the process of outsourcing this particular labor in itself makes it difficult to see what is being lost here. The opportunity that is missed is, in part, the opportunity to notice that something has been missed.

But there is another particular problem with reading a poem by Keats along a positive–negative axis, and any readers of this blog post who are British romanticists will almost certainly have been wondering when I’m going to get to the other half of this problem: the uncritical use of the word “negative” when talking about the poetry of John Keats is complicated by Keats’s specific way of bringing this discussion about “negativity” back to its starting point. I am thinking, of course, of his oft-cited and influential comment about “negative capability” in his letter of 21 December 1817 to his brothers, George and Thomas, in which he defines the phrase “negative capability” to mean

when a man is capable of being in uncertainties, mysteries, doubts, without any irritable reaching after fact and reason – Coleridge, for instance, would let go by a fine isolated verisimilitude caught from the Penetralium of mystery, from being incapable of remaining content with half-knowledge. This pursued through volumes would perhaps take us no further than this, that with a great poet the sense of Beauty overcomes every other consideration, or rather obliterates all consideration.

And it seems that one of the many things that I won’t be able to consider tonight in the depth that it deserves is the way in which this passage suggests a reading of the structure of “Melancholy,” though I would like to say that it does inform my earlier claim that the structure of the ode is “dialectical in a general sense,” and that the concept of negative capability suggests several things about the way in which – and the limits to which – it can be taken as “dialectical.” More, there are clues here about how the third stanza should be taken as reconciling the other two. But all I really have time to talk about tonight in any depth at all is the way in which Sentiment Analysis parses the particular poem by Keats. I will be the first to say that this is unfortunate.

Keats’s point – or, rather, the currently germane of the many substantial points that he makes in this short passage – is that closing off interpretive possibilities by engaging in knee-jerk reactions involves missing what he takes to be one of the major tasks of poetry, and that reductive interpretations of a text or a concept get in the way of higher-order understandings of how the text performs. I think that there is probably more to be said about the tension between the kind of interpretation that Keats suggests in this letter would best be applied to his own poetry, and that kind of interpretation that Sentiment Analysis applies to it. But this will require a closer look at what Sentiment Analysis is actually doing (in brief, as a thumbnail sketch of an answer: I suspect that taking a close look at the algorithm’s emergent patterns of behavior would suggest that the interpretation that it provides is nowhere near as cut and dried as our knee-jerk dismissive assumptions about algorithmic behavior might suggest). This, alas, will have to be yet another task for another night.

References to print texts

Admittedly, there should be some of these in here, in the interest of scholarly honesty. Like so many other tasks that I wish I had time for, this will have to be a task for another time. Unlike those many other tasks, though, I anticipate that this blog post will be edited soon in such a way as to solve this particular problems.


* (Agreed, I should avoid trying to complete assignments at the last minute. However, some weeks in grad school, everything has to be done at the last minute, because there are multiple deadlines, each of which constitutes a crisis. Those interested in seeing my conversation with my less-than-admirable ISP about this and not queasy about the use of the F word can read this tweet, this tweet, this tweet, this tweet, the conversation starting with this tweet, and the conversation resulting from this tweet. As you can probably guess, I am of course thrilled to have a conversation with a company that holds a monopoly on an essential service closed off with a vague promise to pass my complaint on to someone or other, which may or may not mean anything other than that a PR person is going to send an email to an address that routes all of the mail it receives to /dev/null, just as I am of course thrilled by their strong implication that providing maintenance to their equipment absolutely requires shutting down service completely, which is of course not true.)
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