# Conformance Report: Sentiment Engine vs. SENTIENT Spec v2.0.0

**Date:** 2026-08-22  
**Engine Version:** Refactored CryptoSentimentCalibrator + Centroid Layer  
**Spec Reference:** `/root/SENTIMENT_ANALYSIS_ENGINE_SPEC.md` + `IMPLEMENT_GUIDE` + `IMPLEMENT_GUIDE_OPS`

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## Executive Summary

| Spec Section | Status | Conformance | Notes |
|-------------|--------|-------------|-------|
| **Architecture (Sec 2)** | ✅ Implemented | 90% | Two-layer (lexicon + centroid) matches design |
| **Ingestion Contract (Sec 3)** | ❌ Missing | 0% | No ingestion service; engine assumes pre-normalized payloads |
| **NLP Pipeline (Sec 4)** | ✅ Partial | 60% | Entity extraction, sentiment, emotion, event, temporal, credibility implemented but models are mock/placeholder |
| **Event Catalogue (Sec 5)** | ⚠️ Partial | 30% | 150+ types defined in spec; only 12 implemented |
| **Signal Processing (Sec 6)** | ⚠️ Partial | 40% | Event strength formula, velocity, decay, fusion partially implemented |
| **Scoring Engine (Sec 7)** | ✅ Implemented | 80% | fear/greed/hype/pub/pump/dump with centroid refinement |
| **Output Schema (Sec 8)** | ⚠️ Partial | 50% | Core fields present; event_flags format differs |
| **Integration (Sec 9-13)** | ❌ Missing | 0% | No HZ/ClickHouse sinks, no config.yaml, no deployment |
| **Methodology (Sec 16-17)** | ❌ N/A | N/A | TBL/labeling is separate pipeline |

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## Detailed Conformance Analysis

### 1. Architecture — Section 2

| Requirement | Spec | Implemented | Gap |
|-------------|------|-------------|-----|
| High-level pipeline | 7 stages (Ingest → NLP → Event → Signal → Scoring → Aggregation → Sink) | NLP → Event → Signal → Scoring → Aggregation ✅ | Missing Ingestion + Sink |
| Ingestion Service | Kafka/Pulsar/Celery/RQ | ❌ | Not implemented |
| NLP Pipeline | Transformer (RoBERTa NER, FinBERT sentiment, DistilRoBERTa emotion, BERT event) | ✅ Mock/placeholder models | Real models not loaded |
| Signal Processing | Event strength, velocity, decay, fusion | ✅ Core logic | Real-time streaming not implemented |
| Scoring Engine | fear/greed/hype/pub/pump/dump | ✅ | Good |
| Aggregation | Asset → Industry → Market | ✅ | Good |
| Output Sink | Hazelcast ExF + ClickHouse | ❌ | Not implemented |
| Deployment | Separate worker pool + co-located scoring | ❌ | Not deployed |

**Conformance:** 90% of *core* architecture present, but **ingestion and sinks are 0%**.

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### 2. Ingestion Contract — Section 3

| Requirement | Spec | Implemented | Gap |
|-------------|------|-------------|-----|
| Source Categories | 9 categories (crypto news, tradfi, social, exchange, on-chain, regulatory, corporate) | ❌ | No source registry |
| Normalized Payload Schema | 12-field JSON with engagement_metrics | ⚠️ | Schema exists but not validated |
| Source Credibility Registry | Per-source base_credibility + decay | ✅ `source_credibility.yaml` | Registry loaded but not updated via feedback loop |
| Poll Cadences | Defined per category | ❌ | Not implemented |

**Conformance:** 15% — Only the credibility registry exists.

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### 3. NLP Processing Pipeline — Section 4

| Stage | Spec Requirement | Implemented | Gap |
|-------|------------------|-------------|-----|
| **4.1 Preprocessing** | HTML strip, lang detect (fasttext/CLD3), tokenization | ❌ | No preprocessing |
| **4.2 Entity Extraction** | NER (RoBERTa), ticker regex, contract regex, alias resolution (Vitalik→ETH) | ✅ `EntityExtractor` | Uses spaCy (mock) + rule-based; alias map works |
| **4.3 Sentiment Polarity** | FinBERT + emotion (DistilRoBERTa), prompt-based LLM fallback | ✅ `SentimentEmotionAnalyzer` | Models mock; FinBERT calibration works |
| **4.4 Event Classification** | BERT classifier (mrm8488/bert-squadv2), 150+ types, threshold 0.15 | ⚠️ `EventClassifier` | Only 12 types; mock model |
| **4.5 Temporal Anchoring** | HeidelTime + event-type duration priors | ✅ `TemporalAnchorer` | Basic implementation |
| **4.6 Credibility Scoring** | Multi-factor formula (source × recency × detail × author × cross-source × engagement) | ⚠️ `CredibilityScorer` | Partial; missing detail_score, author_rep, engagement_quality |

**Conformance:** 60% — Pipeline structure exists; models are placeholders; event types severely limited.

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### 4. Event Catalogue — Section 5

| Category | Spec Event Types | Implemented | Gap |
|----------|------------------|-------------|-----|
| Tokenomics | 16 (unlock, burn, mint, inflation, etc.) | 0 | — |
| Security & Risk | 13 (hack, exploit, audit, rug pull, etc.) | 1 (`hack`) | 12 missing |
| Technology & Dev | 16 (mainnet, fork, upgrade, SDK, etc.) | 0 | — |
| Governance | 10 (proposal, vote, DAO, etc.) | 0 | — |
| Financial Performance | 19 (earnings, guidance, dividend, analyst, etc.) | 0 | — |
| Market Structure | 24 (listing, delisting, halt, ETF, whale, etc.) | 0 | — |
| Regulatory & Legal | 18 (ban, clampdown, SEC, EU, etc.) | 0 | — |
| News & Media | 10 (mainstream, breaking, rumor, celebrity, etc.) | 0 | — |
| Social & Community | 13 (viral, AMA, quit, pump coord, etc.) | 0 | — |
| DeFi-Specific | 11 (yield, liquid staking, liquidation, etc.) | 0 | — |
| Macro | 15 (Fed, CPI, GDP, geopolitical, etc.) | 0 | — |
| M&A | 10 (announcement, acquisition, partnership, etc.) | 0 | — |
| **TOTAL** | **150+** | **1** | **149 missing** |

**Critical Gap:** Only `EventType.HACK` is implemented. The catalogue is extensible via YAML but no catalogue file exists.

**Conformance:** 30% (structure exists, but content is 99% missing).

---

### 5. Signal Processing Layer — Section 6

| Sub-component | Spec Formula | Implemented | Gap |
|---------------|--------------|-------------|-----|
| **6.1 Event Strength** | `strength = SOURCE_CRED × NUM_SOURCES × DETAIL_FACTOR`<br>SOURCE_CRED = base × recency × author_trust<br>NUM_SOURCES: cross-cluster confirmation<br>DETAIL_FACTOR: dates, amounts, addresses, names, terms, URL | ⚠️ `SignalProcessor._compute_event_strength` | Missing: author_trust, cross-cluster NUM_SOURCES, detail detector model, rumor penalty |
| **6.2 Velocity** | hype_velocity = d(log(mentions_weighted))/dt<br>pub_velocity = d(log(pub_count))/dt<br>EMA α=0.3 | ⚠️ `VelocityComputer` | Uses simplified computation; no real sliding window |
| **6.3 Decay** | `exp(-ln(2) × t / half_life)` per event type | ✅ `TemporalDecay` | Good |
| **6.4 Fusion** | `fused = 100 × (1 - Π(1 - v_i/100))` | ⚠️ `MultiSourceFusion` | Basic implementation |
| **6.5 Cross-Source Bonus** | +20% for different source clusters | ❌ | Not implemented |
| **6.6 Bot Detection** | Echo chamber, coordinated manipulation, bot scoring | ❌ | Not implemented |

**Conformance:** 40% — Core formulas present but missing cross-source intelligence and bot detection.

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### 6. Scoring Engine — Section 7

| Parameter | Spec Formula | Implemented | Conformance |
|-----------|--------------|-------------|-------------|
| **fear_state** | `0.30*fear + 0.25*anger + 0.20*sadness + 0.25*negative_events` | ✅ `SignalProcessor._compute_fear_state` | 85% |
| **greed_state** | `0.35*joy + 0.30*greed + 0.25*positive_events + 0.10*hype` | ✅ `SignalProcessor._compute_greed_state` | 85% |
| **hype_velocity** | BERT centroid cosine similarity + velocity signal | ✅ Centroid refinement | 80% |
| **pub_velocity** | BERT centroid + publication velocity | ✅ Centroid refinement | 80% |
| **pump_score** | BERT centroid + coordination detection | ✅ Centroid refinement | 80% |
| **dump_score** | BERT centroid + negative events | ✅ Centroid refinement | 80% |
| **Centroid Layer** | e5-large-v2 embeddings, cosine similarity, 30% blend | ✅ `CentroidManager` | 90% |

**Key Innovation Delivered:** The spec calls for BERT/cosine centroid refinement — **implemented and working** with real e5-large-v2 encoder (1024-dim).

**Conformance:** 85% — Core scoring + centroid layer working.

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### 7. Output Schema — Section 8

| Field | Spec | Implemented | Gap |
|-------|------|-------------|-----|
| `fear_state` (M,I,A) | 0-100 float | ✅ | |
| `greed_state` (M,I,A) | 0-100 float | ✅ | |
| `hype_velocity` (M,I,A) | -100 to +100 | ✅ | |
| `pub_velocity` (M,I,A) | -100 to +100 | ✅ | |
| `pump_score` (A) | 0-100 | ✅ | |
| `dump_score` (A) | 0-100 | ✅ | |
| `event_flags` (M,I,A) | Array of structured flags | ⚠️ | Format differs from spec |
| `contributing_events` | Dict with drivers | ⚠️ | Partial |
| `last_update_ts` | unix_ts | ✅ | |
| `schema_version` | int | ❌ | Not included |
| `engine_version` | string | ❌ | Not included |

**event_flags Format Gap:**

| Spec Field | Implemented |
|------------|-------------|
| `event_type`, `asset`, `industry` | ✅ |
| `value` (0-100) | ✅ |
| `confidence`, `source_credibility` | ✅ |
| `num_sources`, `detail_factor` | ⚠️ |
| `base_impact`, `t_zero` | ✅ |
| `decay_remaining`, `half_life` | ⚠️ |
| `direction`, `is_scheduled` | ✅ |
| `triggered_at`, `sources` | ❌ |
| `details_extracted` | ❌ |
| `flag_type` (FLAG_TYPE_FOR_EVENT) | ❌ |
| `flags` (sub-tags) | ❌ |

**Conformance:** 50% — Core scores present; event_flags incomplete; missing version fields.

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### 8. Integration & Operations — Sections 9-13

| Requirement | Spec | Implemented | Gap |
|-------------|------|-------------|-----|
| Config (YAML) | `sources.yaml`, `event_catalog.yaml`, `asset_industry_map.yaml` | ⚠️ Partial | Missing `event_catalog.yaml`, `sources.yaml` |
| Hazelcast ExF Sink | `dolphin_features_sentiment` map | ❌ | Not implemented |
| ClickHouse Sink | `exf_data` table | ❌ | Not implemented |
| Real-time Update Cadence | Asset: 5s, Market: 60s | ⚠️ | In-memory only |
| Monitoring/Metrics | Prometheus, OTEL | ⚠️ | Config only |
| Deployment | Worker pool + co-located scoring | ❌ | Not deployed |

**Conformance:** 10% — Configs partially present; no sinks or deployment.

---

### 9. Lexicon & Centroid Layer (IMPLEMENT_GUIDE)

| Component | Spec | Implemented | Notes |
|-----------|------|-------------|-------|
| **Keyword Lists** | 150+ terms per parameter (fear, greed, hype, pub, pump, dump) | ✅ | 2,086 unified weighted terms (-100 to +100) |
| **Sentence Patterns** | Regex templates with weights | ❌ | Not implemented |
| **Semantic Clusters** | Concept clusters with weights | ❌ | Not implemented |
| **BERT Centroid Construction** | Keyword + sentence + cluster weighted mean | ✅ | Built from lexicon via e5-large-v2 |
| **Token Proximity** | Distance from asset mention to keywords | ❌ | Not implemented |
| **Position Weighting** | Recency/primacy bias | ❌ | Not implemented |
| **Temporal Decay** | Half-life per parameter | ✅ | Via scoring config |
| **Confidence Calibration** | Classifier confidence + length factor | ⚠️ | Partial |
| **Centroid Scoring** | Cosine similarity × credibility × decay | ✅ | Working |

**Conformance:** 60% — Centroid layer working; keyword/pattern layer not implemented per spec.

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## Gaps Requiring Action

### P0 — Critical (Blockers for Production)
1. **Ingestion Service** — No way to feed real data
2. **Event Catalogue** — 149/150 event types missing; no YAML catalogue
3. **Output Sinks** — No Hazelcast/ClickHouse persistence
4. **Real Models** — All NLP models are mock/placeholder
4. **Cross-Source Intelligence** — No NUM_SOURCES clustering, no bot detection
5. **Deployment** — No worker pool, no co-located scoring

### P1 — High (Major Spec Divergence)
6. **Event Flags Format** — Missing FLAG_TYPE_FOR_EVENT system, triggered_at, sources, details_extracted
7. **Event Catalogue Loading** — No YAML config for 150+ event types
8. **Detail Factor Detection** — No detail detector (dates, amounts, addresses)
9. **Velocity Computation** — No real sliding window / EMA
10. **Sentence Pattern Matching** — No regex template matching per IMPLEMENT_GUIDE

### P2 — Medium (Quality Improvements)
11. **Semantic Clusters** — No concept cluster weighting
12. **Token Proximity** — No proximity-to-asset scoring
13. **Position Weighting** — No primacy/recency bias
14. **Cross-Source Confirmation** — No cluster-based NUM_SOURCES
15. **Bot Detection** — No echo chamber/coordinated manipulation detection

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## What We HAVE Delivered (Positive)

| Component | Status | Evidence |
|-----------|--------|----------|
| **Unified Weighted Lexicon** | ✅ Complete | 2,086 terms, -100 to +100, priority span matching |
| **Calibration Logic** | ✅ Complete | Lexicon wins on disagreement, amplifies on agreement |
| **Centroid Layer** | ✅ Complete | 6 params × 1024-dim from e5-large-v2 |
| **Centroid Refinement** | ✅ Working | 30% blend in `ScoringEngine._refine_with_centroids` |
| **Core Scoring** | ✅ Complete | fear/greed/hype/pub/pump/dump |
| **Signal Processing** | ✅ Partial | Velocity, decay, fusion structure |
| **Entity Extraction** | ✅ Working | Ticker, contract, alias, NER |
| **Credibility Scoring** | ✅ Partial | Base + recency + cross-source |
| **Temporal Anchoring** | ✅ Basic | TZero + duration |
| **Event Classification** | ⚠️ Structure | Only 12 types implemented |
| **Unit Tests** | ✅ Passing | 46/46 core NLP tests pass |
| **Labeling Pipeline** | ✅ Running | 22 samples processed |

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## Recommendation

The **core two-layer architecture (lexicon + centroid)** is solid and conforms to the spec's methodological intent. However, the system is **not production-ready** without:

1. **Real NLP models** (FinBERT, DistilRoBERTa, BERT event classifier)
2. **Full event catalogue** (150+ types in YAML)
3. **Ingestion service** (RSS/Twitter/Reddit/Exchange/Regulatory)
4. **Output sinks** (Hazelcast + ClickHouse)
5. **Cross-source intelligence** (NUM_SOURCES clustering, bot detection)
6. **Event flag format compliance** (FLAG_TYPE_FOR_EVENT system)

**Next sprint priority:** Implement P0 items to achieve a minimally viable production pipeline.