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 — analyze 70K+ posts with bulk CSV export and Python/pandas. Social listening and brand monitoring powered by 1.5B+ indexed posts.
Best use case
social-sentiment is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using social-sentiment should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/social-sentiment/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How social-sentiment Compares
| Feature / Agent | social-sentiment | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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)`
```bash
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:
```python
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 trendsRelated Skills
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