AI Architecture (Tech Overview)
Sentra’s intelligence layer is built on a robust multi-stage AI pipeline designed specifically for understanding crypto-native sentiment in real time. Unlike general-purpose NLP systems, Sentra’s architecture is optimized to process emotional signals from short, slang-heavy, often speculative content found across crypto platforms.
1. Data Ingestion Layer
Sentra aggregates massive volumes of data from both structured and unstructured sources:
Social platforms: Twitter/X, Discord, Telegram, Reddit
News outlets: Aggregated RSS feeds, crypto media, headline sentiment
On-chain data: Smart contract comments, token transfers with social annotations
Community behavior: Frequency of keyword repetition, emoji usage, sentiment clusters
This layer operates with high frequency, continuously updating input streams with minimal latency.
2. Natural Language Processing (NLP) Engine
At the heart of Sentra is a custom-trained NLP model using transformer-based architectures (BERT, RoBERTa variants) fine-tuned on crypto-specific corpora.
Detects emotional tone (fear, greed, doubt, hype, sarcasm, etc.)
Parses slang, memes, abbreviations, and emojis with contextual meaning
Scores each message based on polarity, intensity, and emotional certainty
Can process multilingual content, prioritizing English, Korean, and simplified Chinese
3. Token Mapping & Signal Routing
After sentiment scoring, messages are contextually matched to tokens, projects, sectors, or influencers:
Uses hashtag and keyword association + knowledge graph (e.g. $SOL ↔ Solana ↔ Ecosystem tags)
Filters spam and unrelated noise using attention-weighted filters
Routes relevant sentiment signals into token-specific scoring pipelines
4. Sentiment Scoring Engine
Each token receives a Sentiment Score (0–100) based on weighted aggregation of:
Emotion type (fear/greed/hype)
Source trust level (verified vs unverified accounts)
Message frequency and clustering behavior
Historical correlation with price movements (machine-learned)
This engine constantly learns and adapts using reinforcement feedback from actual market outcomes.
5. Alert Triggering & Visualization Layer
Once emotional thresholds are crossed, Sentra generates alerts and renders visual insights:
Alert triggers: Sudden shifts in score, rumor spikes, divergent behavior
Visual output: Heatmaps, emotion trendlines, sentiment comparison dashboards
Delivery system: Telegram Mini-App UI, web dashboard, notification API
Architecture Layer
Function
Data Ingestion
Collects multi-source social and on-chain sentiment data
NLP Engine
Interprets tone, slang, memes, and emotion from text and emojis
Token Signal Routing
Maps content to relevant tokens, filters irrelevant noise
Sentiment Scoring Engine
Assigns real-time scores using multi-factor emotional modeling
Alert & Visualization System
Generates alerts and renders sentiment data through UI components
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