MARKET RESEARCH
Land scored reviews in BigQuery for sentiment trend analysis
Scrapes app-store reviews with Apify, scores each with HuggingFace, and loads the enriched rows into BigQuery so analysts can chart sentiment trends and theme volume over time.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily ingestion schedule
- ActionFetch reviews since last loadApify
- ActionScore sentiment + tag themeHugging Face
- LogicMap to schema, dedupe by id
- OutputAppend rows to BigQueryBigQuery
What it does
This builds a clean, queryable history of your app sentiment. It scrapes new reviews via Apify, attaches a HuggingFace sentiment label, score, and extracted theme to each, and appends the enriched rows to a BigQuery table that analysts and dashboards read from.
When to use it
Use it when you want sentiment as a first-class metric in your data stack, not a one-off report. Once reviews land in the warehouse you can trend by version, region, theme, or rating in whatever BI tool you already use.
How it works
- 1A daily schedule triggers ingestion.
- 2An Apify actor fetches reviews posted since the last successful load.
- 3HuggingFace returns a sentiment label, confidence, and theme tag per review.
- 4A logic step maps each review to the warehouse schema and drops rows already loaded by review id.
- 5The new enriched rows are streamed into the BigQuery reviews table for downstream trend analysis.
Set it up
What you configure once, before turning it on.
- 1Connect ApifyActors, scrapers, datasets.
- 2Connect Hugging FaceModels, datasets, spaces — the open-source hub.
- 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.
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