SOCIAL MEDIA

Route Mentions by Sentiment: Thank Fans, Escalate Critics

Splits incoming brand mentions by sentiment so positive ones post to a marketing Slack channel for amplification while angry ones open a Linear issue for human follow-up.

CategorySocial Media
Enginesim
Difficultyintermediate
Triggerschedule
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled brand mention scrapeApify
  • ActionLabel sentiment and summarizeOpenAI
  • LogicBranch: positive vs neutral vs angry
  • OutputPositive: post to marketing SlackSlack
  • OutputAngry: open Linear issue with threadLinearLinear

What it does

A single pipeline that reads every new mention and sends it down one of two paths. Positive mentions go to a Slack channel where marketing can like, repost, or thank the author. Angry mentions are escalated into Linear with the conversation attached for support to handle.

When to use it

When you want one workflow to cover the full emotional range of mentions, keeping marketing and support each focused on the mentions that are theirs, without two separate listeners drifting out of sync.

How it works

  1. 1On a schedule, an Apify actor collects new mentions of your brand.
  2. 2OpenAI labels each mention as positive, neutral, or angry and writes a one-line summary.
  3. 3A router branches on the label.
  4. 4Positive mentions post to the marketing Slack channel with the author link for amplification.
  5. 5Angry mentions create a Linear issue with the full thread and summary for a human to own.
  6. 6Neutral mentions are logged and dropped to keep both surfaces clean.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ApifyActors, scrapers, datasets.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  4. 4
    Connect LinearIssues, projects, cycles, triage.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

Run this workflow in your colony.

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