MARKET RESEARCH
Alert Slack when negative app reviews spike after a release
Watches incoming app-store reviews, scores their sentiment with HuggingFace, and pings a Slack channel the moment the negative-review rate jumps above your baseline so you can…
How it runs
The automated pipeline, trigger to output.
- TriggerShort-interval schedule polls
- ActionFetch reviews since last checkApify
- ActionScore sentiment per reviewHugging Face
- LogicCompare negative share to baseline
- OutputAlert Slack on spikeSlack
What it does
This monitors the stream of new app-store reviews, classifies each with a HuggingFace sentiment model, and tracks the rolling share of negative reviews. When that share spikes past your baseline (for example right after a release), it fires a Slack alert with the offending reviews attached.
When to use it
Use it when a bad release can quietly crater your store rating before anyone notices. This turns review sentiment into an early-warning signal instead of a lagging metric you check weekly.
How it works
- 1A short-interval schedule polls for new reviews.
- 2An Apify actor fetches reviews posted since the last check.
- 3HuggingFace assigns each a sentiment label and score.
- 4A logic step computes the negative share over a rolling window and compares it to your baseline.
- 5If the spike threshold is crossed, a Slack message goes to the on-call channel with the count, the delta, and the worst verbatim reviews; otherwise the run ends silently.
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 SlackChannels, DMs, threads, mentions.
- 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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