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Best prompts for ChatGPT for customer support using sentiment analysis
12 copy-ready prompts to analyze customer sentiment and take operational actions in support workflows, including classification, reply drafting, prioritization, routing, trend detection, multilingual adaptation, training-data generation, compliance flagging, and KPI-driven recommendations.
GPT-5
Claude Opus 4
Claude Sonnet 4
Gemini 2.5 Flash
Gemini 2.5 Pro
You've probably stared at your support dashboard, watching tickets pile up while wondering if that angry customer message needs immediate attention or if it's just someone having a bad day. We've all been there, trying to decode the emotional undertones in customer messages, manually prioritizing tickets, and hoping we're responding with the right tone. It's exhausting to play emotional detective when you're already juggling dozens of support conversations.
These 12 battle-tested ChatGPT prompts transform your customer support from reactive guesswork into proactive, sentiment-driven operations. You'll get ready-to-use prompts for everything from instant sentiment analysis and priority scoring to multilingual responses and escalation prevention. Instead of spending hours manually sorting through customer emotions and crafting responses, you'll have an AI-powered system that automatically detects sentiment, suggests perfect replies, flags compliance risks, and even predicts which customers might churn.
1
Single-message sentiment + key-issues extractor
You are a support assistant. Analyze the following customer message and return ONLY a JSON object with keys: sentiment (Positive/Neutral/Negative), confidence (0.00-1.00), top_issues (array of up to 3 short issue labels), recommended_tag (single string). Do not include explanations. Customer message: "{customer_message}"
Analyze a single customer message and return a short structured JSON: sentiment label, confidence (0-1), 3 most likely issues, and 1 recommended internal tag.
2
Sentiment-aware reply suggestion (short chat)
You are an empathetic support agent. Given the customer message: "{customer_message}", identify sentiment and craft a 1-3 sentence chat reply that matches sentiment-appropriate tone, lists the immediate next step we will take, and provides a clear ETA. Return only the reply text.
Generate a concise agent reply tailored to the customer's sentiment and recommended empathy level. Keep it friendly, 1-3 sentences, include next step and timeframe.
3
Ticket prioritization score using sentiment + SLA
You are a ticket triage engine. For this ticket JSON {"ticket_id":"{ticket_id}","customer_tier":"{customer_tier}","created_hours_ago":{hours},"message":"{customer_message}"} return ONLY a JSON object with: priority (1-100), action (one of: "respond_now","respond_within_1h","respond_within_24h","escalate"), and rationale (1-sentence). Use sentiment and urgency keywords in scoring.
Combine sentiment, customer tier, SLA, and keywords to output a numeric priority (1-100) and a short rationale and recommended SLA action (e.g., escalate, respond within 1 hour).
4
Canned responses by channel and sentiment
Detect sentiment of: "{customer_message}" then output three labeled canned responses: CHAT, EMAIL (subject + body), and SOCIAL_REPLY. For each include the sentiment in the label (Positive/Neutral/Negative). Keep email subject <=70 chars and body <=150 words.
Produce 3 short canned responses (chat, email subject+body, social media reply) adapted to detected sentiment: positive, neutral, negative. Each should be ready to copy-paste.
5
Conversation sentiment timeline and summary
You are a conversation analyst. Input is an array of messages in order: [{"speaker":"Customer/Agent","text":"..."}, ...] supplied as {conversation}. Return ONLY a JSON with: timeline (array of {index, speaker, sentiment, short_reason}), overall_sentiment (Positive/Neutral/Negative), and recommendation (max 20 words).
Given a full conversation (ordered messages with speaker labels), produce a brief timeline of sentiment per message and a 2-sentence summary including overall sentiment shift and recommendation.
6
Escalation risk detector + remediation checklist
Assess escalation risk for: "{customer_message}" (or thread). Return JSON: {"escalation_risk":true/false,"risk_factors":[...],"remediation":[step1,step2,step3]}. Prioritize empathy and recovery actions.
Identify if a message or thread is at risk of escalation and output yes/no, top 3 risk factors, and a 3-step remediation checklist to de-escalate.
7
Batch ticket trend analyzer (weekly)
You are an analytics assistant. Given an array of ticket messages with dates: [{"id":"...","date":"YYYY-MM-DD","message":"..."}, ...], analyze messages from the provided week. Return JSON: {"avg_sentiment_score":-1.0_to_1.0,"percent_negative":0-100,"top_issues":[{issue, count}],"week_over_week_change":"increase/decrease/no_change","recommendation":"..."}.
Process up to N recent ticket messages and return aggregated sentiment metrics, top 5 issues rising vs prior week, and one recommended product/ops action.
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