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NLPInsight Analyzer

Classify financial text sentiment — and understand why domain-specific pre-training matters more than model size.

NLPInsight API

The Problem

Financial markets generate 10,000+ news articles/day. Manual sentiment review costs $50–100/hour per analyst. Automated classification must handle domain nuance: "revenue declined less than expected" is positive in financial context — a pattern that bag-of-words models consistently misclassify.

Why Accuracy Works Here (and F1-Macro as Guard Rail)

The dataset has 3 classes: 58.0% neutral, 26.9% positive, 15.1% negative. Trained on Twitter Financial News Sentiment (11,931 real tweets) — noisy, informal text with stock tickers and abbreviations. F1-macro (0.748) guards the minority negative class — the highest-value signal for risk management.

Metric TF-IDF + LogReg (production) FinBERT (GPU) Why It Matters
Accuracy 80.6% ~85-88%* Honest metric on hard, noisy tweets
F1 (weighted) 0.810 ~0.85* Overall system performance weighted by class frequency
F1 (macro) 0.748 ~0.82* Safety guard: ensures minority negative class isn't neglected

* FinBERT fine-tuning requires GPU. Estimated from published benchmarks.

80.6% on real financial tweets (vs 97% on the easier Financial PhraseBank) is an honest, defensible metric. The dataset upgrade from curated sentences to noisy tweets better demonstrates real-world NLP capability.

Architecture

flowchart TD
    A[Text Input] --> B[SentimentPredictor\nAuto-detect]
    B --> C{model.joblib\nexists?}
    C -->|Yes| D[TF-IDF Vectorizer\nmax 10K features]
    D --> E[LogisticRegression\nclass_weight=balanced]
    C -->|No| F{config.json\nexists?}
    F -->|Yes| G[FinBERT Tokenizer\nmax 256 tokens]
    G --> H[ProsusAI/FinBERT\n110M params]
    H --> I[Classification Head\n+ Softmax]
    E --> J[negative / neutral / positive\n+ confidence scores]
    I --> J

    style D fill:#2d6a4f,color:#fff
    style E fill:#2d6a4f,color:#fff
    style G fill:#7b2cbf,color:#fff
    style H fill:#7b2cbf,color:#fff
    style I fill:#7b2cbf,color:#fff

Green = Production path (TF-IDF, 5ms, 267 MB) · Purple = GPU path (FinBERT, 87ms, 1.4 GB)

Why TF-IDF in production: TF-IDF runs in 5ms (in-pod) with a 267 MB image vs FinBERT's 87ms with a 1.4 GB image. For latency-critical pipelines, the accuracy trade-off (80.6% vs ~88%) is acceptable. The training pipeline supports FinBERT fine-tuning when GPU is available.

Operational

Metric Value Context
Test Coverage 98% (74 tests) CI threshold: 85%
Docker Image 267 MB nlpinsight:v3.5.0 on Artifact Registry (no torch dependency)
Model Size ~5 MB (TF-IDF+LogReg) Downloaded via Init Container from GCS
P50 / P95 Latency 78ms / 140ms (GCP), 100ms / 120ms (AWS) Through ingress, Locust smoke test (6 users)

Responsible AI

  • Fairness: Per-class F1 parity monitored; no class F1 below 0.90
  • Drift: Sentiment distribution monitored via Prometheus (nlpinsight_predictions_total{sentiment}); shift alerts calibrated as relative change from 7-day baseline (not absolute — a market crisis legitimately shifts the distribution)
  • Validation: Pandera schemas for input text and label format

Live Prediction

Swagger UI Sentiment Prediction
Swagger Prediction

Try It

curl -s -X POST http://localhost:8003/predict \
  -H "Content-Type: application/json" \
  -d '{"text":"Fed raises interest rates amid inflation concerns, markets tumble"}' \
  | python3 -m json.tool

Expected: sentiment (negative), confidence (~0.7+), probabilities per class.

curl -s -X POST http://localhost:8003/predict/batch \
  -H "Content-Type: application/json" \
  -d '{"texts":["Revenue beat expectations","Stock crashed after earnings miss","Markets closed flat today"]}' \
  | python3 -m json.tool

Expected: Array of 3 predictions (positive, negative, neutral).

curl -s http://localhost:8003/health | python3 -m json.tool

📄 Full Model Card — includes metric rationale, performance benchmarks, and production decision narrative.

Last Updated: March 2026 — v3.5.3