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name: sentiment-radar description: "Multi-platform sentiment monitoring and analysis for products/brands/topics. Collect public opinions from Chinese platforms (小红书/XHS via MediaCrawler) and English platforms (Twitter/Reddit via Xpoz MCP). Generate structured sentiment reports with product mention tracking, pricing complaints, comparison analysis, and actionable insights. Use when: (1) monitoring competitor sentiment, (2) tracking product launch reception, (3) analyzing user pain points across social media, (4) building market intelligence reports."

Sentiment Radar

Multi-platform social media sentiment collection and analysis.

Supported Platforms

Prerequisites

MediaCrawler (for 小红书)

If not installed:

git clone https://github.com/NanmiCoder/MediaCrawler ~/.openclaw/workspace/skills/media-crawler
cd ~/.openclaw/workspace/skills/media-crawler
uv sync
playwright install chromium

Config: config/base_config.py — set ENABLE_CDP_MODE = True, SAVE_DATA_OPTION = "json"

Xpoz MCP (for Twitter/Reddit)

Requires mcporter with Xpoz OAuth configured. Token at ~/.mcporter/xpoz/tokens.json.

Workflow

Step 1: Define targets

Identify products/brands and search keywords. Example:

Products: Plaud录音笔, 钉钉闪记, 飞书录音豆
Keywords (XHS): Plaud录音笔,钉钉闪记,飞书妙记,AI录音笔评测,录音豆
Keywords (Twitter): Plaud NotePin, DingTalk recorder, Lark voice

Step 2: Collect data

XHS collection

Run MediaCrawler with keywords. Use CDP mode (user's Chrome browser) for anti-detection. The crawler needs QR code scan for login — run in background with exec(background=true).

cd skills/media-crawler
# Update keywords in config/base_config.py, then:
.venv/bin/python main.py --platform xhs --lt qrcode

Environment fixes for macOS:

export MPLBACKEND=Agg
export PATH="/usr/sbin:$PATH"

Data output: data/xhs/json/search_contents_YYYY-MM-DD.json and search_comments_YYYY-MM-DD.json

Twitter/Reddit collection

Use Xpoz MCP tools directly:

• xpoz.getTwitterPostsByKeywords — returns posts with engagement metrics
• xpoz.getRedditPostsByKeywords — returns posts with comments

Step 3: Analyze

Run the analysis script on collected data:

python3 scripts/analyze.py \
  --data ./data \
  --products '{"Plaud": ["plaud","notepin"], "钉钉": ["钉钉","dingtalk","闪记"]}' \
  --output report.md

The script performs:

• Keyword distribution analysis (notes per keyword, total likes/collects)
• Product mention frequency in comments
• Sentiment classification (positive/negative/concern/neutral)
• Top notes ranking by engagement
• Price/subscription complaint extraction
• Product comparison comment extraction

Step 4: Report

The analysis outputs:

1. JSON results to stdout (for programmatic use)
2. Markdown report to --output path

Combine XHS + Twitter data into a comprehensive report. See references/report-template.md for structure.

Key Analysis Dimensions

1. Sentiment split — positive vs negative vs concern ratio
2. Product mentions — which products get discussed most
3. Pricing complaints — subscription fatigue, value perception
4. Comparison comments — head-to-head user opinions
5. User pain points — feature requests, complaints, unmet needs
6. Engagement metrics — likes, collects, shares as popularity signals

Notes

• XHS data uses Chinese number format (e.g., "1.1万") — parse_count() in analyze.py handles this
• MediaCrawler has 2s sleep between requests to avoid rate limiting
• Each keyword returns ~20 notes per page (configurable in MediaCrawler config)
• Comments are fetched per note automatically
• For recurring monitoring, schedule via cron and compare against previous reports