| name | sentiment-analysis-engineer |
| description | This skill should be used when the user asks to build or evaluate sentiment and opinion-mining
systems—label schemas, lexicon/ML/transformer/LLM classifiers, annotation and IAA, F1/calibration,
domain adaptation, multilingual/code-switching, sarcasm/negation/bias, and production inference with
drift monitoring. Triggers: sentiment analysis, sentiment engineer, opinion mining, text
classification, aspect-based sentiment, ABSA, fine-tune sentiment, sentiment model, polarity
detection, emotion detection, review sentiment, social media sentiment, sentiment labeling, sentiment
evaluation, sarcasm detection, multilingual sentiment. Not for marketing copy (content-creator),
MLOps without NLP (ml-ops-engineer), general data science (data-scientist), social campaigns
(social-content), safety-classifier R&D (ml-research-engineer-safeguards), or legal outcomes from
scores alone.
|
Sentiment Analysis Engineer
When to Use
- Define labeling schemas — document-level polarity, aspect-based (ABSA), emotion taxonomies, or multi-label targets
- Choose and implement model stacks — lexicon/rules, classical ML, fine-tuned transformers, or LLM prompt classifiers
- Design annotation programs — guidelines, adjudication, inter-annotator agreement (IAA), and gold-standard refresh
- Run evaluation and error analysis — macro-F1, calibration, confusion slices, and failure-mode catalogs
- Adapt models to domains — product reviews, social posts, support tickets, news, or finance text
- Handle edge cases — negation, sarcasm, entities, code-switching, and demographic or topical bias
- Plan production inference — batch vs streaming, latency budgets, model serving, and API contracts
- Operate monitoring and governance — label drift, score drift, human audit loops, and dashboard integration
When NOT to Use
- Writing marketing copy, brand voice, or content strategy →
content-creator, brand-voice-enforcement
- General ML platform MLOps without sentiment/NLP scope →
ml-ops-engineer
- Exploratory analytics or predictive modeling without text-sentiment focus →
data-scientist
- Social media campaigns, calendars, or channel strategy →
social-content, marketing-analyst
- AI safety classifier or harm-benchmark research only →
ml-research-engineer-safeguards
- Legal, regulatory, or compliance conclusions from sentiment scores alone →
compliance-engineer, legal-risk-assessment
- Production LLM agents, RAG, or copilot features (unless sentiment is one component) →
ai-engineer
- Literature surveys without building or evaluating sentiment systems →
ai-researcher
Related skills
| Need | Skill |
|---|
| Classical ML, A/B tests, general model evaluation | data-scientist |
| LLM apps, RAG, agents, prompt eval harnesses | ai-engineer |
| Papers, benchmarks, research methodology | ai-researcher |
| Warehouse metrics, dbt, analytics pipelines | analytics-engineer |
| Campaign performance and channel ROI | marketing-analyst |
| De-AI-ing prose or editorial voice | content-humanizer |
| MLOps deploy, drift, retraining platform | ml-ops-engineer |
Core Workflows
1. Scope and problem framing
Clarify unit of analysis (document, sentence, span, aspect), label set, languages, latency, and success metrics.
See references/sentiment_analysis_engineer_scope.md.
2. Labeling and annotation
Draft guidelines, pilot batches, measure IAA, and lock gold standards before model training.
See references/labeling_schemas_and_annotation.md.
3. Model selection and training
Compare lexicon, classical, fine-tuned encoder, and LLM-prompt baselines; document tradeoffs and compute.
See references/models_lexicon_transformers_llm.md.
4. Evaluation and error analysis
Report slice metrics, calibration, and qualitative failure buckets with reproduction examples.
See references/evaluation_metrics_and_error_analysis.md.
5. Domain, multilingual, and edge cases
Plan domain adaptation, translation vs native models, and tests for sarcasm, negation, and bias.
See references/domain_multilingual_and_edge_cases.md.
6. Production, monitoring, and governance
Specify serving paths, SLAs, drift monitors, human review queues, and analytics handoffs.
See references/production_serving_monitoring_governance.md.
Outputs
- Problem spec — labels, languages, domains, latency, and acceptance thresholds
- Annotation guide — definitions, examples, edge-case rules, adjudication process
- Model card — data, architecture, metrics, limitations, and known failure modes
- Eval report — headline metrics, slice tables, confusion analysis, calibration plots
- Serving spec — API schema, batch/streaming mode, versioning, and rollback plan
- Monitoring plan — drift metrics, audit sampling, and escalation triggers
Principles
- Treat labels as product decisions — ambiguous guidelines inflate disagreement and model variance
- Always report macro-F1 or per-class recall when classes are imbalanced; accuracy alone misleads
- Separate offline eval from online impact — production KPIs may differ from held-out test F1
- Document residual risk for sarcasm, negation, and out-of-domain text; do not overclaim coverage
- Prefer human-in-the-loop for high-stakes routing; scores support decisions, they do not replace policy
