Sentiment Analysis

Analyze customer sentiment across tickets, reviews, NPS responses, and feedback channels. Surface trends, pain points, and satisfaction drivers with quantified evidence.

Workflow

Step 1: Ingest and Normalize Data

Prepare the input data for analysis:

• Format identification: Detect whether input is CSV, JSON, pasted text, or ticket export.
• Field mapping: Identify the text field (comment, description, body), timestamp, customer ID, and any existing scores.
• Deduplication: Remove duplicate entries from the same customer about the same event.
• Time filtering: Apply the requested period filter if specified.
• Volume check: Note total volume and warn if the sample is too small for reliable trends.

Step 2: Classify Sentiment

Score each piece of feedback:

• Polarity: Positive, negative, neutral, or mixed.
• Intensity: Strong, moderate, or mild (e.g., "frustrated" vs. "slightly annoyed").
• Emotion tags: Specific emotions detected -- angry, confused, delighted, anxious, disappointed, grateful.
• Topic extraction: What feature, process, or interaction the sentiment is about.
• Actionability: Whether the feedback points to something the team can change.

Step 3: Identify Themes

Group individual feedback into patterns:

• Pain points: Recurring negative themes ranked by frequency and intensity.
• Satisfaction drivers: What customers consistently praise.
• Emerging issues: New complaints that were not present in prior periods.
• Resolved issues: Complaints that have decreased since the last analysis.
• Feature-level sentiment: Sentiment breakdown by product area.

Step 4: Analyze Trends

Look at sentiment movement over time:

• Overall trajectory: Is sentiment improving, declining, or stable?
• Inflection points: Dates where sentiment shifted, correlated with releases, incidents, or changes.
• Segment differences: Sentiment by customer tier, region, or use case.
• Volume trends: Are more customers leaving feedback? Fewer? Is silence meaningful?

Step 5: Generate Insights

Translate patterns into actionable recommendations.

Output Format

## Sentiment Analysis Report

**Period**: <date range>
**Sources**: <ticket system, NPS, reviews, etc.>
**Volume**: <total feedback items analyzed>

### Overall Sentiment
- **Positive**: <percentage>
- **Neutral**: <percentage>
- **Negative**: <percentage>
- **Trend**: Improving / Declining / Stable vs. prior period

### Top Pain Points (by frequency)

| Rank | Theme | Mentions | Intensity | Example Quote |
|------|-------|----------|-----------|---------------|
| 1 | <theme> | <count> | High/Med/Low | "<short quote>" |
| 2 | <theme> | <count> | High/Med/Low | "<short quote>" |
| 3 | <theme> | <count> | High/Med/Low | "<short quote>" |

### Satisfaction Drivers

| Rank | Theme | Mentions | Example Quote |
|------|-------|----------|---------------|
| 1 | <theme> | <count> | "<short quote>" |
| 2 | <theme> | <count> | "<short quote>" |

### Emerging Issues
- <new pattern not seen in prior periods>

### Sentiment by Product Area
| Area | Positive | Neutral | Negative | Change |
|------|----------|---------|----------|--------|
| <area> | <pct> | <pct> | <pct> | +/-<pct> |

### Recommendations
1. <actionable recommendation tied to a pain point>
2. <actionable recommendation tied to a pain point>
3. <opportunity to amplify a satisfaction driver>

Examples

User: "Analyze sentiment from last month's NPS responses" Action: Parse NPS comments, classify sentiment and themes, identify top detractor drivers and promoter reasons, produce a trends report.

User: "What are customers saying about the new onboarding flow?" Action: Search tickets and feedback tagged with onboarding, classify sentiment, compare to pre-launch baseline, report findings.

User: "Pull a voice-of-customer report for the Q1 board deck" Action: Aggregate all Q1 feedback sources, quantify sentiment distribution, highlight top themes with quotes, and format for executive consumption.

Best Practices

• Use representative quotes to make themes tangible, but never let quotes substitute for quantified patterns.
• Compare to a prior period. "65% positive" is meaningless without knowing it was 72% last month.
• Separate signal from noise. A single angry review is not a trend; five customers mentioning the same bug is.
• Note sample size and source bias. NPS respondents skew toward strong opinions; ticket data skews negative.
• Correlate sentiment shifts with known events (releases, incidents, pricing changes) to explain causality.
• Flag emerging issues early even if volume is low. A new complaint pattern is more actionable than a chronic one.
• Present negative findings constructively. Frame pain points as opportunities with clear recommendations.
• Segment results when possible. Enterprise sentiment may differ substantially from self-serve.