What This Visualization Shows

This data visualization displays "[OC] I built a heatmap that tracks the emotional profile of financial narratives on Social and Financial media, here's what "geopolitical risk" looks like right now" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on 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](https://narrative-investing.pages.dev/) * **Deeper dives**: We publish methodology explainers and weekly narrative outlooks on [https://narrativeinvesting.substack.com/](https://narrativeinvesting.substack.com/)

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

, which allows us to understand complex relationships and insights within the data through visual storytelling.

Deep Dive into the Topic

Economic data visualization plays a crucial role in understanding market trends, financial performance, and economic patterns across different sectors and regions. This type of data analysis helps economists, policymakers, and business leaders make informed decisions based on quantitative insights.

Economic indicators such as GDP growth, unemployment rates, inflation, and market performance are complex datasets that require sophisticated visualization techniques to communicate effectively. Interactive charts and graphs can reveal trends over time, compare performance across different markets, and highlight correlations between various economic factors.

The significance of economic data visualization extends beyond academic research. Financial institutions use these visualizations for risk assessment, investment strategies, and market analysis. Governments rely on economic data visualization to track policy effectiveness, plan budgets, and communicate economic status to citizens. Businesses use economic trend analysis to forecast demand, plan expansion, and assess market opportunities.

Data Analysis and Insights

The patterns revealed in this visualization demonstrate the importance of systematic data analysis in understanding complex phenomena. By examining different data segments, time periods, and categorical breakdowns, we can identify trends that inform strategic planning and decision-making processes.

Statistical analysis of this data reveals variations across different dimensions that provide insights into underlying drivers and relationships. These patterns help identify areas of opportunity, potential risks, and key performance indicators that can guide future actions and resource allocation.

The analytical approach used in this visualization enables comparison across different categories, time periods, or geographic regions, revealing insights that support evidence-based decision-making. This type of analysis is essential for organizations seeking to optimize performance and understand complex market dynamics.

Significance and Applications

This data visualization has important implications for understanding trends and patterns that affect decision-making across multiple sectors. The insights derived from this analysis can inform policy development, business strategy, resource allocation, and operational improvements.

For analysts, researchers, and decision-makers, this type of data visualization provides essential insights for strategic planning and performance optimization. Whether addressing operational challenges, market analysis, or policy development, understanding data patterns helps create more effective strategies and solutions.

The broader significance lies in how this information contributes to our understanding of complex systems and relationships. This knowledge helps predict future trends, identify potential challenges, and develop more informed approaches to problem-solving and opportunity identification.