sentiment-forecasting-engineer

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Original

English

🇨🇳

Translation

Chinese

Sentiment Forecasting Engineer

When to Use

适用场景

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)

从高流量文本流（社交平台、新闻、评论、调研）构建整体情感指数
设计时间维度聚合规则——按小时、日、周进行聚合，并采用统一的权重规则
预测观点发展轨迹——点预测、预测区间与场景范围
建模情感与业务指标的领先/滞后关系，如销售额、流量、波动率或品牌KPI
选择并实现时间序列与序列模型——ARIMA、Prophet、状态空间模型、TFT等
执行即时预测，并根据决策节奏选择合适的预测周期
基于数据量、速度、主题组合及互动质量构建特征
通过滚动验证法进行回测，并报告不确定性校准结果
处理异常峰值、机器人噪声、样本偏差，以及语言或产品组合的模式转变
将输出结果集成到BI仪表盘、品牌监控或研究工作流中（仅提供方法论）

When NOT to Use

不适用场景

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
```

单文档或片段级极性标注、注释或分类器训练 → 请使用
```
sentiment-analysis-engineer
```
无情感输入的通用需求、库存或物流预测 → 请使用
```
predictive-logistics-developer
```
、
```
data-scientist
```
投资建议、交易推荐或可执行交易信号 → 仅提供预测方法论与不确定性说明
营销文案、活动策划或品牌语调设计 → 请使用
```
content-creator
```
、
```
brand-voice-enforcement
```
无文本情感数据的宏观计量经济或金融建模 → 请使用
```
financial-analyst
```
（仅部分重叠）
静态语料库的探索性NLP或单次情感评分 → 请使用
```
sentiment-analysis-engineer
```
LLM产品功能、Agent或RAG（除非情感预测是其中一个 pipeline 组件） → 请使用
```
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`

需求	技能
文档级极性分析、ABSA（方面级情感分析）、标注、分类器评估	`sentiment-analysis-engineer`
通用机器学习、实验设计、非时间序列预测建模	`data-scientist`
数据仓库指标、dbt、分析流水线（若仓库包含相关内容）	`analytics-engineer`
无观点指数的需求/库存预测	`predictive-logistics-developer`
营销活动ROI与渠道表现（若仓库包含相关内容）	`marketing-analyst`
无文本情感数据的比率、估值、宏观序列分析（若仓库包含相关内容）	`financial-analyst`
LLM应用、Agent产品的特征存储	`ai-engineer`

Core Workflows

核心工作流程

1. Scope and index design

1. 范围与指数设计

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

See
references/sentiment_forecasting_engineer_scope.md
.

明确目标群体（品牌、产品、地域）、文本来源、聚合粒度、目标周期及下游KPI。

参考文档：
references/sentiment_forecasting_engineer_scope.md

2. Indices, aggregation, and features

2. 指数、聚合与特征工程

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

See
references/indices_aggregation_and_features.md
.

定义指数计算公式、聚合规则、主题/分层拆分及协变量（数据量、速度、组合）。

参考文档：
references/indices_aggregation_and_features.md

3. Time-series and forecast models

3. 时间序列与预测模型

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

See
references/time_series_and_forecast_models.md
.

选择基准模型与进阶模型；匹配季节性、节假日及外部驱动因素。

参考文档：
references/time_series_and_forecast_models.md

4. Backtesting, validation, and metrics

4. 回测、验证与指标

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

See
references/backtesting_validation_and_metrics.md
.

滚动验证评估、区间校准及峰值事件保留测试。

参考文档：
references/backtesting_validation_and_metrics.md

5. Data quality, bias, and events

5. 数据质量、偏差与事件处理

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

See
references/references_data_quality_bias_and_events.md
.

机器人过滤、样本偏差处理、语言漂移检测及场景分析的冲击标注。

参考文档：
references/references_data_quality_bias_and_events.md

6. Production monitoring and stakeholders

6. 生产监控与 stakeholder 沟通

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

See
references/production_monitoring_and_stakeholders.md
.

服务节奏、漂移监控、仪表盘约定及面向利益相关者的成果说明。

参考文档：
references/production_monitoring_and_stakeholders.md

Outputs

输出成果

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)

指数规范——计算公式、覆盖范围、权重、分层规则与修订策略
特征目录——已构建的信号及其定义与滞后结构
预测规范——周期、频率、模型类别及外部输入
回测报告——滚动验证指标、区间覆盖度及失败切片分析
即时预测手册——延迟预算、刷新规则及 stale-data 处理方案
监控计划——漂移、峰值告警及人工审核触发条件
利益相关者简报——包含明确不确定性的轨迹说明（无交易建议）

Principles

核心原则

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

预测整体趋势而非个体观点——指数稳定性与定义清晰度优先
将指数构建视为模型的一部分——权重变更会破坏历史可比性
针对时序数据，优先采用滚动验证而非单一保留集拆分
报告预测区间与场景，而非仅提供点估计；披露回测中的覆盖度
区分方法论与决策建议——不得将预测作为买卖或确定性结果呈现
在每个发布的指数旁，记录已知偏差（平台组合、机器人占比、人口统计偏差）

sentiment-forecasting-engineer

Original

Translation

Sentiment Forecasting Engineer

Sentiment Forecasting Engineer

When to Use

适用场景

When NOT to Use

不适用场景

Related skills

相关技能

Core Workflows

核心工作流程

1. Scope and index design

1. 范围与指数设计

2. Indices, aggregation, and features

2. 指数、聚合与特征工程

3. Time-series and forecast models

3. 时间序列与预测模型

4. Backtesting, validation, and metrics

4. 回测、验证与指标

5. Data quality, bias, and events

5. 数据质量、偏差与事件处理

6. Production monitoring and stakeholders

6. 生产监控与 stakeholder 沟通

Outputs

输出成果

Principles

核心原则