Sentiment Analysis with ShedBox Agent
Analyze customer sentiment across feedback, reviews, and support tickets.
Analyze Any Text Data
You: "Analyze the sentiment of these customer reviews"
ShedBox Agent:
✓ Analyzed 1,247 reviews
Sentiment Overview:
- Positive: 68% (848 reviews)
- Neutral: 22% (274 reviews)
- Negative: 10% (125 reviews)
Overall Sentiment Score: 7.2/10
Top Positive Themes:
1. "Easy to use" - mentioned 234 times
2. "Great support" - mentioned 189 times
3. "Time saver" - mentioned 156 times
Top Negative Themes:
1. "Slow loading" - mentioned 45 times
2. "Missing features" - mentioned 38 times
3. "Pricing" - mentioned 29 times
Use Cases
Customer Reviews
"Analyze sentiment in our App Store reviews"
"What are customers saying about the new feature?"
"Compare sentiment before and after the update"
Support Tickets
"Categorize support tickets by customer emotion"
"Identify frustrated customers who need attention"
"What issues are causing the most negative feedback?"
Social Media
"Analyze brand mentions on Twitter"
"Track sentiment around our product launch"
"Compare our sentiment to competitors"
Survey Responses
"Analyze open-ended NPS responses"
"Categorize feedback by topic and sentiment"
"What drives detractors vs promoters?"
Example Workflow
You: [Uploads support_tickets.csv]
You: "Analyze customer sentiment and identify issues"
ShedBox Agent:
✓ Loaded 3,456 support tickets
✓ Analyzing text content...
📊 Sentiment Analysis Results
Overall Distribution:
- 🟢 Positive/Satisfied: 42%
- 🟡 Neutral/Informational: 38%
- 🔴 Negative/Frustrated: 20%
Trend (Last 30 Days):
[Line chart showing sentiment over time]
⚠️ Negative sentiment increased 5% after Oct 15 update
Topic Analysis:
| Topic | Volume | Sentiment | Trend |
|-------|--------|-----------|-------|
| Login issues | 234 | 🔴 -0.7 | ↑ 23% |
| New UI | 189 | 🟡 0.1 | New |
| Billing | 156 | 🟡 0.2 | → |
| Speed | 142 | 🔴 -0.5 | ↑ 15% |
| Features | 98 | 🟢 0.4 | ↓ 5% |
Urgent Attention:
23 tickets marked as "angry" or "frustrated" requiring escalation
You: "Show me the tickets about login issues"
ShedBox Agent:
Login Issues Analysis (234 tickets):
Common Patterns:
1. "Password reset not working" - 67 tickets
2. "2FA issues" - 45 tickets
3. "Session timeout" - 38 tickets
4. "SSO not connecting" - 28 tickets
Sample Negative Ticket:
"I've been trying to log in for 2 hours! Password reset emails aren't
coming through. This is unacceptable for a paid product."
Sentiment: -0.9, Urgency: High
Recommendation: Investigate email delivery issues with password reset
Generated Pipeline
Sentiment analysis becomes automated:
data_sources:
tickets:
type: csv
path: support_tickets.csv
ai_interface:
provider: anthropic
prompts:
analyze_sentiment:
system: "You are a sentiment analysis expert."
user_template: |
Analyze the sentiment of this support ticket:
{{ticket_content}}
Return:
- sentiment_score: -1 to 1
- sentiment_label: positive/neutral/negative
- topics: list of topics mentioned
- urgency: low/medium/high
- key_phrases: important phrases
processing:
transform:
- operation: ai_enrich
source_field: ticket_content
prompt: analyze_sentiment
aggregate:
group_by: [topic, week]
metrics:
- avg_sentiment: avg(sentiment_score)
- ticket_count: count
- negative_ratio: count(sentiment_label == 'negative') / count
output:
type: file
path: sentiment_report.json
Analysis Features
| Feature | Description |
|---|---|
| Sentiment Score | Numeric score from -1 (negative) to 1 (positive) |
| Topic Extraction | Automatic identification of topics discussed |
| Entity Recognition | Identify products, features, people mentioned |
| Urgency Detection | Flag tickets needing immediate attention |
| Trend Analysis | Track sentiment changes over time |
| Comparative Analysis | Compare sentiment across segments |
Get Started
Understand your customer sentiment at scale.