| 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 — 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 or instant trial) — auth via xpoz-setup skill"},"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_..."
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
😊 58% | 😠 24% | 😐 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
