Ive build a model that quantifies narratives. The AI boom narrative has been top of mind the past weeks, however the associated emotion is changing.

for months it ran on greed and optimism. now fear and panic are the squares lighting up while the positive reads fade. story still loud, mood underneath flipping.

anyone else feeling the AI trade get heavier? not advice

Attention on the inflation narrative just jumped 4 ranks in a week. Sitting at #3 now, 93rd percentile over the past year.

What's interesting is the emotion mix underneath it. Optimism z-score at -1.76, hope at -0.95, greed at -0.82. Confidence is up slightly (+0.48) but fear (+0.40) and panic (+0.34) are quietly building. Basically the market is paying attention again but it's not panicking yet. That gap is usually where the interesting stuff happens.

Feels like the setup nobody wants to acknowledge. CPI prints have been "fine" enough to keep the fed pivot crowd hopeful, but the chatter is shifting. Stagflation talk hasn't fully arrived but is also rising

Been tracking this and the rotation back to inflation as a top-tier narrative is one of the cleaner moves I've seen in a few weeks.

I've been working on a side project that uses an LLM pipeline to track how financial narratives evolve across Financial news and social media discussion. Not just how much people are talking about something, but how they feel about it, broken down into ten distinct emotions, tracked over time.

The attached heatmap shows the emotion z-scores for the geopolitical_risk narrative over the past month (April 2026). Each row is an emotion, each column is a day, and the color intensity shows how far above or below normal that emotion is running.

How to read it

• Green for negative emotions (panic, fear, frustration, skepticism, uncertainty) = those emotions are below their baseline, ie people are calmer than usual
• Red for negative emotions = those emotions are elevated, people are more afraid/frustrated than normal
• The scale flips for positive emotions (optimism, confidence, hope, greed, euphoria) --> red means elevated positive sentiment, green means it's suppressed
• Z-scores range from roughly -3 to +2.5, so you're looking at standard deviations from the mean

What stands out

A couple of things jump out in this particular snapshot:

End/mid of March was dominated by fear. The fear row lights up deep red at the start of the month, a clear spike well above +2 standard deviations. That's a lot of anxiety concentrated in a short window.

Then it faded end of March. By mid-April, the negative emotions (panic, fear, frustration) all shifted to light green. People essentially got used to the geopolitical headlines. The narrative didn't go away, but the emotional charge drained out of it.

The recent flip is interesting. Right at the end of the chart (around April 28), you can see the positive emotions, optimism, hope, greed, euphoria, starting to tick red while confidence stays muted. That's a pattern worth watching: people getting greedy about a narrative that was scaring them three weeks ago.

Confidence and greed diverged early on. At the start of April, confidence was elevated (green) while greed was also elevated (green), but they moved in opposite directions through the month. Greed without confidence is a different animal than greed with confidence.

The bigger picture

This is one chart from a larger dashboard that tracks financial narratives across News and Social media. For each narrative, the system calculates attention scores, z-scores (day-over-day and week-over-week), percentile rankings, emotion profiles like this one, and an SIR epidemic model that classifies whether a narrative is currently spreading, crowding, or exhausting.

The emotion heatmap is probably my favorite view because it surfaces things you can't see from volume alone. Two narratives can have identical attention scores but completely different emotional signatures (and that difference matters).

Data & tools

• Data source: Financial discussion, processed with a custom LLM pipeline (entity recognition, emotion classification, narrative tagging)
• Emotion model: Ten-emotion classifier (panic, fear, frustration, skepticism, uncertainty, optimism, confidence, hope, greed, euphoria), scored as z-scores against rolling baselines using EWMA smoothing
• Stack: Python for data processing, custom frontend for the dashboard (and yes, some vibecoding on the front end)
• Dashboard: narrative-investing.pages.dev
• Deeper dives: We publish methodology explainers and weekly narrative outlooks on https://narrativeinvesting.substack.com/

Happy to answer questions about the pipeline, the emotion classification approach, or anything else under the hood.