Reply To: Can AI Detect Real-Time Brand Sentiment Shifts on Social Media?

Nice work — you’ve captured the essentials. Two quick framing points before the checklist: treat the AI output as a signal enhancer, not an autopilot; and prioritize speed + a clear human review path for anything flagged urgent. That keeps you responsive without chasing noise.

What you’ll need:

• Access to your mentions stream (platform API, native alerts, or a connector).
• An AI sentiment endpoint or lightweight model that can return polarity, intensity and a confidence score.
• A simple collector (sheet, DB, or small dashboard) and an alert channel (Slack/email/SMS).
• A short response playbook with named owners and three canned actions: Acknowledge, Investigate, Resolve.

How to set it up (step-by-step):

1. Capture mentions every 10–15 minutes from one channel to start; store text, author, timestamp and engagement metrics.
2. Submit text to your AI endpoint and record: sentiment (P/N/Neut), intensity (1–5), topic tags (max 3), and confidence (0–1).
3. Compute a rolling 24-hour sentiment score and compare it to a 7-day baseline; calculate negative volume and sentiment velocity (hourly rate of change).
4. Set two alert rules to begin: 1) 24h score drop >15% vs 7d baseline; 2) negative volume up >50% vs previous 24h. Route alerts to Slack and push items with confidence >0.7 and intensity ≥4 into a manual review queue.
5. Run a 48–72 hour calibration: review false positives, add common sarcasm/slang examples to the review notes, and adjust thresholds or topic filters.

What to expect:

• First 48–72 hours: frequent false positives as you learn language quirks. Don’t overreact — tune thresholds and topic filters.
• After tuning: fewer, higher-quality alerts; faster time-to-first-response and clearer correlation with downstream KPIs (traffic, conversions, churn).

Prompt pattern (concise, non-copyable): Ask the model to return labeled fields only — sentiment, intensity, up to three topic tags, an urgency value and a confidence score — and ask it to flag likely sarcasm or ambiguous language. Keep the instruction short and explicit; avoid long formatting rules.

Variants to consider:

• Precision-first: bias instructions toward conservative negative labels and require higher confidence before surfacing alerts (useful for small teams).
• Recall-first: bias toward catching every potential negative mention and route low-confidence items to a human triage queue (useful for high-risk brands).
• Multilingual/Broad: add a language-detection step and lightweight translations for non-English mentions before sentiment scoring.

Concise tip: track false positives and false negatives as a simple ratio each week and optimize the prompt/thresholds to improve that metric — you’ll get more signal without hiring engineers.