social-sentimentSentiment 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 via ClawdBot CLI:
clawdbot install atyachin/social-sentimentAnalyze brand sentiment from live social conversations at scale.
Surfaces themes, flags viral complaints, compares competitors. Analyzes 1K-70K posts via bulk CSV + Python.
Run xpoz-setup skill. Verify: mcporter call xpoz.checkAccessKeyStatus
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 5sRepeat for Reddit/Instagram. Default: 30 days.
Use dataDumpExportOperationId, poll with checkOperationStatus for download URL (up to 64K rows).
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.
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.
Download full CSVs | Reddit = honest | Store data/social-sentiment/ for trends
Generated Mar 1, 2026
A consumer electronics company uses this skill to track sentiment around a new product launch on Twitter and Reddit, identifying early complaints about battery life and positive feedback on design. They analyze 20,000 posts over 30 days to adjust marketing and prioritize fixes, preventing a potential PR crisis.
A SaaS startup monitors sentiment for its main competitors across Instagram and Twitter to understand user pain points and strengths. By analyzing 15,000 posts, they uncover that competitors are praised for customer support but criticized for pricing, informing their own positioning and feature development.
A retail brand uses this skill to scan social media for viral complaints about a product recall or service issue on Reddit and Twitter. They process 10,000 posts in real-time, quickly identifying negative sentiment spikes and themes like shipping delays, enabling rapid response and damage control.
A consumer packaged goods company analyzes sentiment on Instagram and Reddit for a new beverage line, tracking keywords like 'taste' and 'packaging'. They export 5,000 posts to CSV, using Python to classify sentiment and extract themes, guiding product improvements and marketing campaigns.
A media agency monitors sentiment around a political campaign or entertainment event across Twitter and Reddit to gauge public opinion. They analyze 30,000 posts to surface themes and viral content, providing insights for editorial coverage and audience engagement strategies.
Offer a free tier with limited post analysis (e.g., up to 1,000 posts) to attract small businesses, then charge for premium features like bulk CSV exports (up to 70K posts), advanced sentiment themes, and competitor tracking. Revenue comes from monthly subscriptions based on usage tiers.
Provide social listening and brand monitoring as a service to clients, using this skill to generate detailed sentiment reports and insights. Charge per project or retainer for ongoing monitoring, crisis detection, and competitive analysis, leveraging the skill's scalability to handle multiple brands.
License aggregated sentiment data and insights derived from analyzing social media posts to market research firms, investors, or large corporations. Use the skill's bulk export capabilities to process millions of posts, selling anonymized datasets or trend reports on brand reputation and industry shifts.
š¬ Integration Tip
Set up the xpoz-setup skill first for OAuth authentication, then use mcporter commands to query social platforms and poll for operation status before downloading CSVs for analysis in Python.
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