Sentiment Analysis

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Overview

Stanna analyzes the emotional tone of client communications across Gmail, HubSpot, and Intercom. Sarcasm detection and context-aware scoring provide insights into client satisfaction and relationship health.

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How Sentiment Analysis Works

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Sentiment Scale

Stanna uses a -2 to +2 numerical scale for precise sentiment tracking:

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Analysis Methods

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Sentiment Weighting & Impact

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Directional Weighting

Communication direction affects sentiment impact:

• Inbound negative: Higher weight (client expressing dissatisfaction)
• Outbound negative: Lower weight (you addressing issues)
• Inbound positive: High weight (client expressing satisfaction)
• Outbound positive: Moderate weight (your positive communications)

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Temporal Weighting

Recent sentiment carries more weight than historical sentiment:

• Last 7 days: 100% weight
• 8-30 days: 70% weight
• 31-90 days: 40% weight
• Over 90 days: 20% weight

This ensures current relationship status is prioritized over past issues that may have been resolved.

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Sarcasm Detection

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How Sarcasm Detection Works

Stanna uses AI models to detect sarcasm and irony in client communications, ensuring that seemingly positive messages with sarcastic intent are correctly identified as negative sentiment. Detection Methods:

• AI-Powered Analysis: Uses AI models trained on sarcasm detection
• Pattern Recognition: Identifies common sarcastic phrases and structures
• Context Clues: Analyzes punctuation patterns, contradictory statements, and exaggerated language

Common Sarcasm Patterns Detected:

• Exaggerated positive phrases: “Oh great!”, “Just wonderful”, “Exactly what I needed”
• Quotation marks around positive words: “This is ‘great’”
• Contradictory statements: “This is perfect… but it doesn’t work”
• Excessive punctuation: Multiple exclamation marks or mixed punctuation
• Common sarcastic expressions: “Thanks a bunch”, “Brilliant”, “Couldn’t be better”

Sentiment Adjustment: When sarcasm is detected with high confidence (>70%), the apparent sentiment is flipped:

• Apparent positive becomes negative
• The adjusted sentiment reflects the true negative intent
• Confidence levels are maintained for transparency

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Content Analysis Details

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Subject Line Analysis

Email subject lines provide immediate sentiment indicators:

• Urgent/Escalation: “URGENT”, “Problem”, “Issue”, “Help needed”
• Positive: “Thank you”, “Great meeting”, “Success”, “Appreciation”
• Neutral: “Meeting notes”, “Follow-up”, “Update”, “Question”
• Negative: “Disappointed”, “Concerned”, “Problem”, “Not working”

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Content Snippet Processing

Stanna analyzes key portions of message content:

• Opening and closing phrases: Tone-setting language
• Key sentence sentiment: Main message emotional content
• Context clues: References to satisfaction, issues, or future plans
• Professional formality: Changes in communication style

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Sentiment Attribution

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Understanding Sentiment Changes

Track what drives sentiment variations:

• Specific interactions: Which emails or conversations affected scores
• Content triggers: Particular words or phrases that influenced analysis
• Trend patterns: How sentiment is changing over time
• Contextual factors: Business events that correlate with sentiment changes

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Manual Sentiment Overrides

When automated analysis misinterprets context:

1. Access client interaction history
2. Identify incorrectly analyzed interactions
3. Use manual attribution to provide correct context
4. Document reasoning for future reference

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Integration-Specific Considerations

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Gmail Sentiment Patterns

Professional Email Norms:

• Formal business communication tends toward neutral baseline
• Urgency doesn’t always indicate negative relationship health
• Thank you messages and positive feedback carry high positive weight
• Issue reporting is expected and manageable if followed by resolution

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Intercom Support Context

Support Communication Expectations:

• Customers contacting support often start with neutral or slightly negative sentiment
• Resolution quality significantly impacts final sentiment score
• Multiple tickets about the same issue indicate deeper problems
• Quick, effective resolution often results in positive sentiment

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HubSpot Business Intelligence

Deal and Meeting Context:

• Meeting attendance and engagement indicate relationship investment
• Deal progression stagnation may reflect underlying concerns
• Contract negotiations can temporarily affect sentiment
• Long sales cycles require different sentiment interpretation

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Best Practices for Sentiment Management

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Proactive Monitoring

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Communication Style Impact

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Advanced Sentiment Features

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Sentiment Trending

Track sentiment patterns over time:

• Rolling averages: 7-day, 30-day, and 90-day sentiment trends
• Seasonal patterns: Identify recurring sentiment cycles
• Intervention effectiveness: Measure how actions affect sentiment recovery
• Predictive indicators: Early warning signs of sentiment decline

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Stakeholder-Level Sentiment

For clients with multiple contacts:

• Individual sentiment tracking: Monitor each stakeholder separately
• Aggregate sentiment: Overall account relationship health
• Influence weighting: Key decision-maker sentiment carries more weight
• Sentiment divergence: When different stakeholders show different sentiment patterns

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Sentiment-Based Automation

Configure automated actions based on sentiment:

• Alert thresholds: Notifications when sentiment drops below specified levels
• Task creation: Automatic follow-up tasks for negative sentiment interactions
• Escalation triggers: Alert management when sentiment reaches critical levels
• Success celebration: Recognize and reinforce positive sentiment achievements

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Troubleshooting Sentiment Analysis

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Privacy & Data Handling

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Sentiment Data Security

• Content analysis: Only analyzes content for sentiment, doesn’t store complete message text
• Encryption: All sentiment data encrypted at rest and in transit
• Access control: Sentiment details only available to authorized team members
• Data retention: Sentiment scores retained according to workspace policies

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Compliance Considerations

• GDPR compliance: Sentiment analysis respects data processing preferences
• Opt-out options: Clients can request exclusion from automated sentiment analysis
• Data minimization: Only processes content necessary for sentiment evaluation
• Transparency: Clear documentation of how sentiment analysis affects client relationships