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