Data & Research

Survey Response Analysis Agent

SurveysNLPSentiment AnalysisResearch

Difficulty5/10 · Intermediate

Market: HighMVP: 2-3 weeks

Overview

An agent that processes thousands of free-text survey responses and transforms them into structured insights. It identifies recurring themes, performs sentiment analysis, detects emerging concerns, groups related feedback, and generates executive summaries with specific recommendations. Handles multiple languages and adapts to domain-specific terminology in customer feedback, employee surveys, or market research.

Problem It Solves

Companies collect thousands of open-ended survey responses but lack the resources to analyze them properly. Manual coding of qualitative data is expensive and subjective. Most survey tools only handle quantitative data, leaving rich qualitative insights locked in unread text fields.

How It Works

A Preprocessor agent cleans and normalizes response text across languages. A Theme Extractor agent clusters responses into coherent topics using embedding-based similarity. A Sentiment Analyzer agent scores each response and theme. A Reporter agent synthesizes findings into an executive summary with statistical backing and specific quotes.

Getting Started

1
Set up CrewAI with Preprocessor, Theme Extractor, Sentiment Analyzer, and Reporter agents
2
Connect to your survey platform (SurveyMonkey, Typeform, Qualtrics) via API
3
Configure domain-specific vocabulary and topic categories
4
Build visualization tools for theme distribution and sentiment trends
5
Create exportable reports in formats compatible with your presentation tools

Claude Code Prompt

Build a Survey Response Analysis Agent — a production-ready agent with full implementation details, architecture, constraints, and verification steps.

## Tech Stack
- Framework: ...
- Language: Python
- Key integrations: ...

## Goal
Create a working MVP that...

## Implementation Steps
1. Project Setup: Initialize...
2. Configure agents and tools...
3. Build the core pipeline...

Unlock Full Details

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