Social Sentiment OpenClaw Plugin & Skill | ClawHub
Looking to integrate Social Sentiment into your AI workflows? This free OpenClaw plugin from ClawHub helps you automate devops & cloud tasks instantly, without having to write custom tools from scratch.
What this skill does
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 ā analyze 70K+ posts with bulk CSV export and Python/pandas. Social listening and brand monitoring powered by 1.5B+ indexed posts.
Install
openclaw skills install @atyachin/social-sentimentnpx clawhub@latest install social-sentimentFull SKILL.md
Open original| name | description | homepage | tags |
|---|---|---|---|
| social-sentiment | 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 ā analyze 70K+ posts with bulk CSV export and Python/pandas. Social listening and brand monitoring powered by 1.5B+ indexed posts. | https://xpoz.ai | sentiment-analysisbrand-monitoringsocial-mediatwitterredditinstagramanalyticsbrand-sentimentreputationsocial-listeningopinion-miningbrand-trackingcompetitor-analysispublic-opinioncrisis-detectionNLPreputationmcpxpozopinionmarket-research |
SKILL.md content below is scrollable.
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
š 58% | š 24% | š 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