Structured Methods—Narrative Tagging, Sentiment Scoring, and Event Graphs

1. Narrative Tagging System: Turning Text Into “Groupable Event Language”

The first step in narrative research is mapping news and social media content to a unified tag space. The tagging system should meet three criteria:

• Balance of mutual exclusivity and exhaustiveness: covers major narrative types without tag explosion;
• Cross-platform transferability: the same tag applies to texts from different sources;
• Traceability: every tag can be traced back to original evidence and timestamp.

A three-level tag structure is commonly used in practice:

Level 1 Tags (Macro Level)

• Examples: Regulation, macro liquidity, geopolitical risks, systemic security events.
• Used to judge whether a narrative has “market-wide spillover” potential.

Level 2 Tags (Sector Level)

• Examples: Public chain ecosystems, DeFi, NFT, GameFi, payments, infrastructure, etc.
• Used to locate the main battlegrounds of capital rotation.

Level 3 Tags (Asset Level)

• Examples: Specific projects, tokens, protocol upgrades.
• Used to map narratives to tradable objects.

The value of narrative tags is that they turn “stories” into “groupable time series,” allowing statistical testing of narrative strength, duration, and asset correlation.

2. Sentiment Scoring: Upgrading From “Positive/Negative” to “Sentiment Structure”

Traditional sentiment analysis often outputs a single score: positive or negative. In crypto markets, a single score is often misleading because the same event can trigger both greed and fear (e.g., “regulatory clarity = less uncertainty but short-term selling pressure increases”).

A more robust approach is to construct a “sentiment structure vector” with at least four dimensions:

• Valence: overall bullish or bearish tilt (−1 = strongly bearish, 0 = neutral, 1 = strongly bullish);
• Arousal: intensity of discussion and emotional sharpness;
• Dispersion: degree of divergence among different groups’ views;
• Confidence: whether the narrative is stated as “established fact” or “rumor/speculation.”

Dispersion is often overlooked in practice but usually explains volatility better than valence:

When a community moves from divergence to consensus, price trends are more likely to accelerate; when consensus breaks down into divergence, trends are more likely to exhaust.

3. Diffusion Scoring: Measuring Whether a Narrative Is “Truly Spreading” or Just “Artificially Hyped”

Social media hype is easily manipulated; therefore, diffusion scoring should focus on structure over total volume. Common structural indicators include:

• Diffusion radius: whether discussion spreads from core nodes to a broader spectrum of accounts;
• Cross-platform resonance: whether the same narrative heats up simultaneously across multiple platforms;
• Rate of new participant entry: whether the proportion of new users joining the discussion is rising;
• Homogeneity index: whether the proportion of repetitive phrasing is abnormally high (indicating bot activity).

The key issue in diffusion scoring is whether rising hype corresponds to genuine attention shift.

If only the total volume increases but the diffusion radius doesn’t expand, the narrative is likely a short-term pulse—trading assumptions about its persistence should be reduced.

4. Event Graphs: Connecting “Isolated News” Into an “Inferable Network”

The toughest challenge in narrative trading is information fragmentation—the same theme appears repeatedly across different times and channels.

The purpose of an event graph is to organize discrete information into a network structure:

• Nodes: events (news, announcements, key social posts, on-chain abnormal transfers);
• Edges: causal relationships, temporal sequence, topic similarity, entity association;
• Weights: source credibility, propagation level, capital correlation strength.

Event graphs enable three key capabilities:

1. Narrative merging: consolidating repeated and variant information into a single storyline to reduce noise;
2. Narrative fork identification: detecting competing interpretation paths for the same event;
3. Narrative decay monitoring: when new edges decrease and node isolation rises, it often signals narrative decay.

The value of event graphs lies in upgrading “text research” into “dynamic system research,” making them more suitable as monitoring and alert frameworks.

5. On-chain Validation Layer: Aligning Narrative Scores With Capital Evidence

Without on-chain validation, narrative scores easily degenerate into pure text speculation. The alignment method usually adopts a “dual threshold”:

• Narrative threshold: narrative strength and diffusion structure reach minimum tradable standards;
• Capital threshold: observable on-chain or trading structure alignment appears (e.g., sustained net inflows, changes in address behavior patterns).

Only when both layers are met does it move to strategy mapping; if only the narrative layer is satisfied, it’s more suitable for risk observation and event research.

This mechanism shifts narrative trading from “believing stories” to “verifying whether stories have capital consequences.”

6. Layered Output of Indicator Systems: Research Signals vs Trading Signals

To avoid overfitting and misuse, outputs should be clearly layered:

• Research-level indicators: used for market interpretation, hypothesis building, and report generation;
• Monitoring-level indicators: used for early warning, identifying narrative shifts and abnormal diffusion;
• Trading-level indicators: used to trigger position and risk control rules—these must be stricter and more robust.

Many failures stem from directly using research-level indicators as trading-level ones.

Layered output acknowledges that interpreting the market and making consistent profits are different goals requiring different thresholds and validation standards.

7. Common Pitfalls: Structured Doesn’t Mean “More Complex”

Common mistakes in structured methods include:

• Overly granular tags: leading to sparse samples and overfitting;
• Static sentiment lexicons: unable to adapt to new memes, phrases, or narrative templates;
• Ignoring time alignment: treating lagging on-chain evidence as immediate triggers;
• Treating hype as alpha: equating increased discussion with higher probability of price rise.

The goal of structuring should be “maintainable,” not “all-encompassing.”

The long-term viability of an indicator system depends on clear updating and monitoring mechanisms—not on the sheer number of metrics.

8. Lesson Summary

This lesson completes a key leap in narrative trading methodology—from information collection to indicator-based systematization.

Key takeaways include:

• Establishing a three-level narrative tagging system to make text information groupable and statistically analyzable;
• Expanding sentiment scoring into structural vectors to improve explanations for volatility and trend reversals;
• Using diffusion structure metrics to distinguish genuine hype from manipulative hype;
• Integrating fragmented information with event graphs for narrative merging, forking, and decay monitoring;
• Aligning narrative scoring with capital evidence via on-chain validation to reduce pure text trading risk.

The next lesson moves into execution: mapping scores to trades—focusing on how to translate narrative and sentiment metrics into position sizing, frequency, and risk control rules while handling execution risks from crowded trades and expectation gaps.