- Data collection
- Reviews (as above).
- Historical tourist arrival/visitor counts (by month or week) for each region/destination.
- Other features: weather, holidays, airfare prices, events.
- Sentiment features extraction
- Use generic sentiment (positive/negative/neutral) on whole reviews or daily aggregated sentiment.
- Or deeper: average sentiment by aspect (ABSA), or use embeddings / topic-level sentiment.
- Feature engineering
- Lag features, moving averages, sentiment deltas (how sentiment changed month to month), seasonality features.
- Predictive modeling
- Build forecasting models (e.g. ARIMA, Prophet, LSTM, Random Forest, XGBoost) to predict future visitor counts or tourism revenue, using sentiment features as regressors.
- Compare models with vs without sentiment features to test the added value of sentiment.
- Scenario simulation & impact estimation
- Simulate how improving sentiment in some months or aspects would shift the forecast.
- E.g. “If sentiment improved 10% during off-peak months, visitors might increase by X number, leading to Y economic gain.”
- Validation & robustness
- Back-testing and cross-validation.
- Sensitivity analyses (which sentiment features matter most?).
- Check overfitting, look at residuals, error metrics, etc.
- Report + recommendations
- Point out which sentiment signals are the strongest predictors of demand.
- Provide strategic advice: “focus on reviews in January–March,” or “invest in improving sentiment in destination Y because it helps more with demand elasticity.”
This template is closer to the forecasting / impact quantification side, which is a “value-add” over just sentiment analysis.