sentiment-forecasting-engineer

Original：🇺🇸 English

Translated

This skill should be used when the user asks to forecast aggregate sentiment and opinion dynamics over time—sentiment indices from text streams; temporal rollups; leading/lagging KPI links; time-series and sequence models (ARIMA, Prophet, state-space, ML); nowcasting; spikes, bots, and bias; walk-forward backtests; intervals and scenarios; volume/velocity/topic features; BI or brand dashboards. Triggers: sentiment forecasting, forecast sentiment, sentiment index, opinion trend forecast, social sentiment time series, brand sentiment trajectory, nowcast sentiment, sentiment leading indicator, aggregate polarity forecast, sentiment backtest, walk-forward sentiment, sentiment spike prediction. Not for per-text labeling (sentiment-analysis-engineer), demand forecasting without sentiment (predictive-logistics-developer, data-scientist), trade advice (methodology only), marketing copy (content-creator), macro without text sentiment (financial-analyst partial).

2installs

Sourcedaemon-blockint-tech/agentic-enteprises-skill

Added on2026-05-20

NPX Install

npx skill4agent add daemon-blockint-tech/agentic-enteprises-skill sentiment-forecasting-engineer

SKILL.md Content

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Sentiment Forecasting Engineer

When to Use

Build aggregate sentiment indices from high-volume text streams (social, news, reviews, surveys)
Design temporal rollups — hourly, daily, weekly aggregation with consistent weighting rules
Forecast opinion trajectories — point forecasts, prediction intervals, and scenario bands
Model leading/lagging relationships between sentiment and sales, traffic, volatility, or brand KPIs
Select and implement time-series and sequence models — ARIMA, Prophet, state-space, TFT, etc.
Run nowcasts and choose forecast horizons aligned to decision cadence
Engineer features from volume, velocity, topic mix, and engagement quality
Backtest with walk-forward validation and report calibration of uncertainty
Handle spikes, bot noise, sample bias, and regime shifts in language or product mix
Integrate outputs with BI dashboards, brand monitoring, or research workflows (methodology only)

When NOT to Use

Per-document or per-span polarity labeling, annotation, or classifier training →
```
sentiment-analysis-engineer
```
Generic demand, inventory, or logistics forecasting without sentiment inputs →
```
predictive-logistics-developer
```
,
```
data-scientist
```
Investment advice, trade recommendations, or actionable trading signals → provide forecasting methodology and uncertainty only
Marketing copy, campaigns, or brand voice →
```
content-creator
```
,
```
brand-voice-enforcement
```
Broad macro econometrics or financial modeling without text-derived sentiment →
```
financial-analyst
```
(partial overlap only)
Exploratory NLP or single-shot sentiment scores on a static corpus →
```
sentiment-analysis-engineer
```
LLM product features, agents, or RAG (unless sentiment forecasting is one pipeline component) →
```
ai-engineer
```

Related skills

Need	Skill
Document-level polarity, ABSA, annotation, classifier eval	`sentiment-analysis-engineer`
General ML, experimentation, non-time-series predictive modeling	`data-scientist`
Warehouse metrics, dbt, analytics pipelines (if present in repo)	`analytics-engineer`
Demand/inventory forecasting without opinion indices	`predictive-logistics-developer`
Campaign ROI and channel performance (if present in repo)	`marketing-analyst`
Ratios, valuation, macro series without text sentiment (if present)	`financial-analyst`
LLM apps, feature stores for agent products	`ai-engineer`

Core Workflows

1. Scope and index design

Clarify population (brand, product, geo), text sources, aggregation grain, target horizon, and downstream KPIs.

See
references/sentiment_forecasting_engineer_scope.md
.

2. Indices, aggregation, and features

Define index formulas, rollups, topic/strata splits, and covariates (volume, velocity, mix).

See
references/indices_aggregation_and_features.md
.

3. Time-series and forecast models

Choose baselines and advanced models; align seasonality, holidays, and exogenous drivers.

See
references/time_series_and_forecast_models.md
.

4. Backtesting, validation, and metrics

Walk-forward evaluation, interval calibration, and spike-event holdouts.

See
references/backtesting_validation_and_metrics.md
.

5. Data quality, bias, and events

Bot filtering, sample bias, language drift, and shock labeling for scenario analysis.

See
references/references_data_quality_bias_and_events.md
.

6. Production monitoring and stakeholders

Serving cadence, drift monitors, dashboard contracts, and stakeholder-ready narratives.

See
references/production_monitoring_and_stakeholders.md
.

Outputs

Index specification — formula, universe, weights, strata, and revision policy
Feature catalog — engineered signals with definitions and lag structure
Forecast spec — horizon, frequency, model family, and exogenous inputs
Backtest report — walk-forward metrics, interval coverage, and failure slices
Nowcast playbook — latency budget, refresh rules, and stale-data handling
Monitoring plan — drift, spike alerts, and human review triggers
Stakeholder brief — trajectory narrative with explicit uncertainty (no trade advice)

Principles

Forecast aggregates, not individual opinions — index stability and definitional clarity come first
Treat index construction as part of the model — changing weights invalidates historical comparability
Prefer walk-forward evaluation over single holdout splits for time-ordered data
Report intervals and scenarios, not point estimates alone; disclose coverage on backtests
Separate methodology from decisions — do not present forecasts as buy/sell or guaranteed outcomes
Document known biases (platform mix, bot share, demographic skew) beside every published index