Social Sentiment
by @atyachin
Sentiment analysis for brands and products across Twitter, Reddit, and Instagram. Monitor public opinion, track brand reputation, detect PR crises, surface complaints and praise at scale β analyze 70K+ posts with bulk CSV export and Python/pandas. Social listening and brand monitoring powered by 1.5B+ indexed posts.
clawhub install social-sentimentπ About This Skill
name: social-sentiment description: "Sentiment analysis for brands and products across Twitter, Reddit, and Instagram. Monitor public opinion, track brand reputation, detect PR crises, surface complaints and praise at scale β analyze 70K+ posts with bulk CSV export and Python/pandas. Social listening and brand monitoring powered by 1.5B+ indexed posts." homepage: https://xpoz.ai metadata: { "openclaw": { "requires": { "bins": ["mcporter"], "skills": ["xpoz-setup"], "network": ["mcp.xpoz.ai"], "credentials": "Xpoz account (free tier) β auth via xpoz-setup skill (OAuth 2.1)", }, "install": [{"id": "node", "kind": "node", "package": "mcporter", "bins": ["mcporter"], "label": "Install mcporter (npm)"}], }, } tags: - sentiment-analysis - brand-monitoring - social-media - twitter - reddit - instagram - analytics - brand-sentiment - reputation - social-listening - opinion-mining - brand-tracking - competitor-analysis - public-opinion - crisis-detection - NLP - reputation - mcp - xpoz - opinion - market-research
Social Sentiment
Analyze brand sentiment from live social conversations at scale.
Surfaces themes, flags viral complaints, compares competitors. Analyzes 1K-70K posts via bulk CSV + Python.
Setup
Run xpoz-setup skill. Verify: mcporter call xpoz.checkAccessKeyStatus
4-Step Process
Step 1: Search Platforms
Queries: (1) "Brand" (2) "Brand" AND (slow OR buggy) (3) "Brand" AND (love OR amazing)
mcporter call xpoz.getTwitterPostsByKeywords query='"Notion"' startDate="YYYY-MM-DD"
mcporter call xpoz.checkOperationStatus operationId="op_..." # Poll 5s
Repeat for Reddit/Instagram. Default: 30 days.
Step 2: Download CSVs
Use dataDumpExportOperationId, poll with checkOperationStatus for download URL (up to 64K rows).
Step 3: Analyze
Python/pandas:
import pandas as pd
df = pd.read_csv('/tmp/twitter-sentiment.csv')POSITIVE = ['love', 'amazing', 'best', 'recommend']
NEGATIVE = ['hate', 'terrible', 'worst', 'broken']
def classify(text):
t = str(text).lower()
pos = sum(1 for k in POSITIVE if k in t)
neg = sum(1 for k in NEGATIVE if k in t)
return 'positive' if pos>neg else ('negative' if neg>pos else 'neutral')
df['sentiment'] = df['text'].apply(classify)
Extract themes, find viral by engagement. Customize keywords.
Step 4: Report
Sentiment: 72/100 | Posts: 14,832
π 58% | π 24% | π 18%Themes: Performance (2K, 81% neg), UX (1.8K, 72% pos)
Viral: [Top 10]
Score: Engagement-weighted, 0-100. Include insights.
Tips
Download full CSVs | Reddit = honest | Store data/social-sentiment/ for trends
βοΈ Configuration
Run xpoz-setup skill. Verify: mcporter call xpoz.checkAccessKeyStatus
π Tips & Best Practices
Download full CSVs | Reddit = honest | Store data/social-sentiment/ for trends