ClawHub

Sentiment Radar

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.

Audits

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Install

openclaw skills install sentiment-radar

Sentiment Radar

Multi-platform social media sentiment collection and analysis.

Supported Platforms

PlatformMethodAuth Required
小红书 (XHS)MediaCrawler (CDP browser)QR code login
TwitterXpoz MCP (xpoz.getTwitterPostsByKeywords)OAuth token
RedditXpoz MCP (xpoz.getRedditPostsByKeywords)OAuth token

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