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) — 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
😊 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