- Data collection
- Scrape reviews from Google Maps, TripAdvisor, Instagram captions/comments, travel blogs for destinations in NT (or similar region).
- Optionally, pull related data: visitor count records, economic stats, events calendar, transport access, weather.
- Preprocessing & cleaning
- Clean text, remove noise, detect language, standardize, remove duplicates.
- Possibly break reviews into sentences.
- Aspect / topic extraction
- Using topic modeling (LDA, BERTopic), or guided by a tourism domain ontology (e.g. “transport”, “accommodation”, “cleanliness”, “food & drink”, “activities”, “safety”).
- Use unsupervised or semi-supervised methods if you lack labels.
- Aspect-based sentiment analysis (ABSA)
- For each review or sentence, assign sentiment (positive, negative, neutral) to each aspect.
- Use existing models (e.g. BERT-based ABSA, or fine-tune) or LLM zero-shot methods (as in the Discovering sentiment insights paper).
- Optionally also detect emotions (joy, anger, etc.).
- Aggregate and analyze
- For each destination or region, compute aspect-level sentiment averages or distributions.
- Rank which aspects are strengths vs weaknesses.
- Detect trends over time (seasonality), or sentiment shifts (e.g., before/after events).
- Cross-correlate sentiment with external metrics (visitor numbers, economic returns, seasonality) to see what aspects drive growth.
- Recommendation modeling / intervention simulation
- Suppose you “improve sentiment on X aspect by Y%” — simulate or estimate what effect that would have on visitor numbers or economic value.
- Build a regression or forecasting model where sentiment variables are features to predict visitor counts or tourism revenue.
- Use scenario analysis: e.g. “If sentiment around transport improves by 20%, projected visitor count increases by Z% next year.”
- Visualization & dashboard
- Map-based sentiment overlays (geospatial).
- Time-series charts.
- Word clouds / keyword clouds by aspect.
- “Sentiment heatmap” across aspects / destinations.
- Presentation & insights
- Highlight top pain points and actionable insights (e.g. “invest in transport connectivity”, “upgrade facilities at site X”, “better signage / information”).
- Discuss limitations, biases, and risks.
- Optionally, provide a prototype “tourist feedback monitoring dashboard” for stakeholders.
This template fits well with the competition’s demands (text → insights → actionable recommendations → forecasting).