modbender Sentiment Radar
Multi-platform social media sentiment collection and analysis.
Supported Platforms
| Platform | Method | Auth Required |
|---|---|---|
| 小红书 (XHS) | MediaCrawler (CDP browser) | QR code login |
Xpoz MCP (xpoz.getTwitterPostsByKeywords) |
OAuth token | |
Xpoz 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 metricsxpoz.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:
- JSON results to stdout (for programmatic use)
- Markdown report to
--outputpath
Combine XHS + Twitter data into a comprehensive report. See references/report-template.md for structure.
Key Analysis Dimensions
- Sentiment split — positive vs negative vs concern ratio
- Product mentions — which products get discussed most
- Pricing complaints — subscription fatigue, value perception
- Comparison comments — head-to-head user opinions
- User pain points — feature requests, complaints, unmet needs
- 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