Published by TOSScoin Research

Crypto Sentiment Signal APIs: Beyond Price Monitoring

Key Findings

Source: https://academy.santiment.net/sanapi/
Finding: API is GraphQL-first, with query-based metric retrieval, batching flexibility, and documented pages for rate limits/complexity/restrictions.
Why it matters: Enables custom, low-noise monitoring queries for watchlists instead of generic dashboard scraping.

Source: https://academy.santiment.net/metrics/social-dominance/
Findings:
- Social dominance compares asset conversation share against broader crypto discussion baseline.
- Exposes source-scoped variants: Telegram, Reddit, Twitter, Bitcointalk, YouTube, 4chan, Farcaster, plus total and moving averages/change metrics.
- Metric updates can be revised with label/source recalculations and supplements.
Why it matters: Gives a practical way to spot where narrative momentum is actually forming and avoid over-weighting a single channel.

Source: https://docs.messari.io/api-reference/endpoints/signal/sentiment/overview
Findings:
- Provides sentimentScore, sentimentMomentumScore, positive/negative/neutral post percentages, and tweet volume.
- Documents endpoint categories for list, detail, and time-series workflows.
- Methodology references supervised sentiment scoring across high-mindshare social posts.
Why it matters: Supports systematic "sentiment shift" alerts that can be cross-checked against holder-concentration and liquidity-risk signals.

Build a daily sentiment panel for a target meme cohort (top concentration-risk tokens and peers).
Trigger alerts on unusual sentiment-momentum spikes not confirmed by healthy holder structure.
Use source-specific dominance (e.g., Farcaster/Telegram split) to identify which channels are driving narrative.
Combine sentiment with whale-distribution data to avoid amplifying "hype-only + whale-heavy" setups.