MARKET RESEARCH
Themed review dataset pipeline to BigQuery
Daily, scrapes reviews for your app and competitors, classifies each into a complaint theme and sentiment.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule
- ActionScrape new reviews since watermarkApify
- LogicDedupe against already-loaded review IDs
- ActionLabel each review with theme, sentiment, severityOpenAI
- OutputAppend labeled rows to BigQueryBigQuery
What it does
This is the data-warehouse backbone behind every review dashboard. Each day it scrapes reviews across your app and a competitor set, uses an LLM to label each review with a complaint theme, sentiment, and severity, then appends the clean, structured rows to a BigQuery table. Analysts build trend charts and theme-share-over-time views on top without ever touching raw scraping.
When to use it
Use it when you've outgrown one-off briefs and want review themes as a queryable dataset — to join against releases, correlate with ratings, or feed a BI tool.
How it works
- 1A daily schedule kicks off the run.
- 2Apify scrapes new reviews for every tracked app since the last watermark.
- 3A dedupe step drops reviews already loaded, keyed on review ID.
- 4OpenAI labels each remaining review with theme, sentiment, and severity in a structured batch.
- 5The labeled rows are appended to the BigQuery table, partitioned by date and app, ready for downstream dashboards.
Set it up
What you configure once, before turning it on.
- 1Connect ApifyActors, scrapers, datasets.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect BigQueryDatasets, queries, schemas.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.