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
Real data. Real impact.
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Open source
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Analyze 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-setupmcporter call xpoz.checkAccessKeyStatusQueries: (1)
"Brand""Brand" AND (slow OR buggy)"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.
Use
dataDumpExportOperationIdcheckOperationStatusPython/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 😊 58% | 😠 24% | 😐 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/No automatic installation available. Please visit the source repository for installation instructions.
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