Automated Sentiment Analysis for Tickets and Reviews
Implements AI-powered sentiment analysis to automatically classify tickets, reviews, and mentions, generating real-time alerts and dashboards.
Automate sentiment classification across all feedback channels to detect crises early, prioritize negative interactions, and measure brand perception in real time.
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Prompt objective
Automate sentiment classification across all feedback channels to detect crises early, prioritize negative interactions, and measure brand perception in real time.
Real use case
BeautyGlow, a D2C cosmetics brand with 45,000 customers, receives 3,200 interactions per week across tickets (support platform), reviews (website + marketplace), comments (social platforms), and mentions (X/Twitter). Currently, the team classifies sentiment manually, processing only 20% of volume.
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Prompt
Implement an automated sentiment analysis system for [COMPANY NAME], operating in the [INDUSTRY] sector, processing [NUMBER] interactions per week. **Step 1 — Channel and Volume Mapping:** | Channel | Volume/Week | Format | Current Tool | |-------|---------------|---------|------------------| | Support tickets | [NUMBER] | Text | [SUPPORT TOOL] | | Website reviews | [NUMBER] | Text + rating | [PLATFORM] | | Social comments + DMs | [NUMBER] | Text + image | Meta Business | | X/Twitter mentions | [NUMBER] | Text | - | | Google Reviews | [NUMBER] | Text + rating | Google Business | | Consumer complaint sites | [NUMBER] | Text | [PLATFORM] | **Step 2 — Classification Model:** - **Sentiment**: Positive / Neutral / Negative (with 0-100 score) - **Emotion**: Satisfaction, Frustration, Anger, Surprise, Disappointment, Gratitude - **Urgency**: Low, Medium, High, Critical - **Theme**: [Industry-specific categories] - **Intent**: Information, Complaint, Praise, Suggestion, Legal threat **Step 3 — Technical Implementation:** - Option A (no-code): [SaaS tool — MonkeyLearn/Idiomatic/Chattermill] - Option B (low-code): Sentiment analysis API (Google NLP / AWS Comprehend / Azure Text Analytics) + [AUTOMATION TOOL: Zapier/Make] - Option C (custom): Python + NLP models trained on Portuguese text - Recommended model for Portuguese: [BERTimbau / multilingual-sentiment] - Training dataset: historically classified tickets For each option: estimated monthly cost, implementation time, expected accuracy. **Step 4 — Automations and Alerts:** - Instant alert for negative sentiment with high urgency: - Slack/Teams to CS team - Email to manager - Automatic prioritization in [SUPPORT TOOL] - Crisis alert: >10 negative mentions about the same theme within 1 hour - Opportunity alert: spike in positive mentions (positive viral) - Smart routing: negative tickets to more experienced agents **Step 5 — Sentiment Dashboard:** - Overall sentiment score (real-time) - Weekly/monthly trend by channel - Word cloud by sentiment - Top 10 positive and negative themes - Sentiment by product/service - Comparison with previous periods - Correlation: sentiment vs. sales vs. churn **Expected ROI:** - Time saved: [HOURS/WEEK] previously spent on manual classification - Crisis detection: from [HOURS] to minutes - Retention impact: prioritizing detractors reduces churn by [PERCENTAGE]% Available budget: [BUDGET]/month. Technical team available: [YES/NO]. Primary tool: [TOOL].
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