Feedback Sentiment Analyzer

Pro v1.0.0 1 view

Analyze sentiment across hundreds to thousands of customer feedback items, identifying trends, drivers, and actionable insights with comprehensive reports and alerts.

What You Get

Transform 4-6 hours of manual sentiment classification into a 2-minute automated analysis. Quickly identify what customers love, what frustrates them, and which issues require urgent attention across all feedback channels.

The Problem

Product managers and customer experience teams struggle to analyze sentiment across large volumes of feedback from support tickets, NPS surveys, app reviews, and social media. Manual sentiment classification is time-consuming, inconsistent, and often results in missed patterns or delayed response to critical issues. Teams need to understand overall sentiment health, track changes over time, segment by product/customer type, and identify root causes driving satisfaction or dissatisfaction - but existing tools require expensive enterprise software or technical expertise to set up and use.

The Solution

This skill analyzes sentiment at scale by processing CSV files containing customer feedback. It classifies each item as positive, negative, neutral, or mixed using comprehensive sentiment scoring that accounts for context, negations, and intensifiers. The skill aggregates results by user-defined segments (product area, customer type, feedback source), tracks trends over time when dates are provided, and extracts themes by analyzing keyword frequency and patterns. It generates actionable reports with executive summaries, segment comparisons, sentiment drivers, and prioritized recommendations with urgency levels. The enhanced CSV output includes sentiment labels for further analysis in other tools.

How It Works

  1. 1 Upload CSV and specify feedback text column, optional date column for trends, and metadata columns for segmentation
  2. 2 Validate data quality by checking for empty/duplicate/very short feedback and report data quality summary
  3. 3 Classify sentiment for each feedback item using positive/negative language detection, negation handling, intensifiers, and context analysis
  4. 4 Aggregate sentiment by overall distribution, metadata segments, time periods, and specific topics/keywords
  5. 5 Extract sentiment drivers by identifying top themes through keyword frequency analysis, phrase extraction, and pattern grouping with representative quotes
  6. 6 Generate comprehensive report with executive summary, segment breakdowns, trend analysis, sentiment drivers, alerts for concerning patterns, and prioritized action recommendations

What You'll Need

  • CSV file with at least 50 feedback items (200+ recommended for trend analysis)
  • One column containing feedback text with full sentences preferred over keywords
  • Optional: Date/timestamp column for trend analysis over time
  • Optional: Metadata columns for segmentation by product area, customer type, or source
  • Feedback in English (other languages require translation preprocessing)

Get This Skill

Requires Pro subscription ($9/month)

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Examples

Checkout Flow Issue Prioritization

Analyzes 200 customer feedback items about checkout flow issues to prioritize fixes. Identifies crash (28%) and payment (28%) issues as top problems, reveals mobile checkout is significantly worse than desktop (46% vs 35% negative), and provides prioritized fix recommendations with estimated revenue impact ($75K-$150K monthly improvement potential).

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Mobile App Update Crisis Detection

Analyzes 500 feedback items from Twitter and support channels about a mobile app update over 6 days to determine if it's a crisis. Identifies severe crisis with 53% negative sentiment (vs 20-30% baseline), shows deteriorating day-by-day trend (38% to 58% negative), and provides immediate action plan including rollback/hotfix recommendations.

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NPS Survey Analysis by Customer Tier

Analyzes 350 NPS survey comments segmented by customer tier (enterprise, pro, free) to identify satisfaction gaps. Reveals a critical 117-point satisfaction gap between Pro tier (+66 net score) and Free tier (-51 net score), identifies mobile crashes and pricing as key pain points for free users, and provides a prioritized 30-60-90 day action plan.

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