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sentiment-forecasting-engineer

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Sentiment Forecasting Engineer

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

相关技能

NeedSkill
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）