Beta Survey Analysis

通过 Python 脚本读取含 respondent_id 与 response 列的 CSV，使用 BERTopic 与 GPT-4o-mini 对开放式问卷回答做主题聚类和情感检测，最终输出包含情感分布、主要主题和行动建议的 Markdown 报告，运行需 Python 环境及 OpenAI 或 Anthropic API key。

1477009639zw-blip

热度 37.67

最近更新 2026-05-11 17:37:34

分类：数据依赖：代码执行 / 读写文件 / 内部调用大模型 / 第三方 API应用场景：数据处理 / 报告撰写 / 研究 / 摘要总结

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add betasurvey

Metadata

name: survey-analysis
description: AI-powered survey response analysis. Analyzes open-ended survey responses, clusters themes, detects sentiment, and generates actionable insights. Uses BERTopic + GPT-4o-mini.
metadata: {"openclaw":{"emoji":"📋","requires":{"bins":["python3"]},"always":false}}

Survey Response Analysis Tool

AI-powered analysis of open-ended survey responses. Clusters themes, detects sentiment, generates actionable insights.

Usage

python3 analyze.py --input responses.csv --output report.md

Input CSV Format

respondent_id,response
1,"The product is great but delivery is slow"
2,"Amazing quality, fast shipping"
3,"Good but expensive"

Output

# Survey Analysis Report

## Sentiment Distribution
- Positive: 60%
- Neutral: 25%  
- Negative: 15%

## Top Themes
1. Delivery Speed (mentioned 45%)
2. Product Quality (mentioned 38%)
3. Price Value (mentioned 22%)

## Action Items
- Improve delivery logistics
- Maintain quality standards

Notes

Requires: python3, pandas, openai (or Anthropic API key)