Verified:GitHub

sentiment-analysis

Analyze user feedback to compute sentiment scores and identify market segments.

Updated Mar 30, 2026

Search Tags:#segmentation #nps #thematic-analysis #user-feedback #sentiment-analysis #customer-insights #survey-analysis

Ready to run

npx skills add https://github.com/omeragaakbas/zoyare --skill sentiment-analysis-omeragaakbas

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: sentiment-analysis
Source: https://github.com/omeragaakbas/zoyare/tree/main/.claude/skills/sentiment-analysis
Command: npx skills add https://github.com/omeragaakbas/zoyare --skill sentiment-analysis-omeragaakbas

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill summarizes large volumes of user feedback into clear, segment-level insights so teams can identify who is satisfied or at risk and what product changes will move the needle.

Core Features & Use Cases

Segment identification: Detect at least three distinct user segments or personas from reviews, surveys, or support transcripts.
Thematic synthesis: Extract recurring themes, pain points, and positive feedback per segment and surface representative quotes.
Sentiment scoring & prioritization: Assign sentiment scores (-1 to +1), estimate satisfaction drivers, and prioritize issues by frequency and business impact.
Use Case: Run on a CSV of product reviews to produce segment profiles, NPS proxies, and 2-3 high-impact product recommendations.

Quick Start

Analyze the attached CSV of product reviews to produce segment profiles with sentiment scores, top themes, and prioritized recommendations.

Frequently Asked Questions about sentiment-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I analyze user feedback from CSV exports for sentiment and segmentation?▼

User feedback analysis processes CSV exports by extracting text, identifying at least three user segments, assigning sentiment scores from -1 to +1, and surfacing prioritized product recommendations based on frequency and business impact.

Can I extract recurring themes and pain points from survey responses automatically?▼

Survey responses undergo thematic synthesis to extract recurring themes, pain points, and positive feedback per segment. Representative quotes are cited alongside sentiment scores to highlight satisfaction drivers and prioritize issues.

Does user feedback sentiment analysis work with PDFs and social listening outputs?▼

Sentiment analysis handles large-scale inputs including PDFs, review datasets, and social listening outputs. It requires text extraction from files before computing sentiment scores and synthesizing thematic insights across detected user segments.

What is the best way to compute NPS proxies from large-scale product review datasets?▼

Product review datasets are processed to detect user segments, compute sentiment scores, and generate NPS proxies. The analysis prioritizes issues by frequency and business impact, yielding 2-3 high-impact product recommendations per segment.

How do I prioritize product changes based on customer insights from support transcripts?▼

Customer insights from support transcripts are prioritized by estimating satisfaction drivers and ranking issues according to frequency and business impact, producing segment-level profiles and actionable product recommendations.

What limitations exist when running thematic analysis on mixed-format user feedback?▼

Thematic analysis on mixed-format user feedback requires text extraction as a prerequisite. Processing large-scale inputs across CSVs, PDFs, and social listening outputs depends on data cleanliness to accurately detect segments and assign sentiment scores.