xai-sentiment
π―Skillfrom adaptationio/skrillz
|Performs real-time sentiment analysis on Twitter/X topics using Grok's AI, providing nuanced insights into public opinion and emotional tone.
Installation
npx skills add https://github.com/adaptationio/skrillz --skill xai-sentimentSkill Details
Real-time sentiment analysis on Twitter/X using Grok. Use when analyzing social sentiment, tracking market mood, or measuring public opinion on topics.
Overview
# xAI Sentiment Analysis
Real-time sentiment analysis on Twitter/X content using Grok's native integration and built-in NLP capabilities.
Quick Start
```python
import os
from openai import OpenAI
client = OpenAI(
api_key=os.getenv("XAI_API_KEY"),
base_url="https://api.x.ai/v1"
)
def analyze_sentiment(topic: str) -> dict:
"""Analyze sentiment for a topic on X."""
response = client.chat.completions.create(
model="grok-4-1-fast",
messages=[{
"role": "user",
"content": f"""Analyze sentiment on X for: {topic}
Search recent posts and return JSON:
{{
"topic": "{topic}",
"sentiment": "bullish" | "bearish" | "neutral",
"score": -1.0 to 1.0,
"confidence": 0.0 to 1.0,
"positive_percent": 0-100,
"negative_percent": 0-100,
"neutral_percent": 0-100,
"sample_size": number,
"key_themes": ["theme1", "theme2"],
"notable_posts": [
{{"author": "@handle", "summary": "...", "sentiment": "..."}}
]
}}"""
}]
)
return response.choices[0].message.content
# Example
result = analyze_sentiment("$AAPL stock")
print(result)
```
Sentiment Score Scale
| Score Range | Label | Description |
|-------------|-------|-------------|
| 0.6 to 1.0 | Very Bullish | Strong positive sentiment |
| 0.2 to 0.6 | Bullish | Moderately positive |
| -0.2 to 0.2 | Neutral | Mixed or balanced |
| -0.6 to -0.2 | Bearish | Moderately negative |
| -1.0 to -0.6 | Very Bearish | Strong negative sentiment |
Sentiment Analysis Functions
Basic Sentiment
```python
def get_basic_sentiment(query: str) -> dict:
"""Get simple sentiment score."""
response = client.chat.completions.create(
model="grok-4-1-fast",
messages=[{
"role": "user",
"content": f"""Search X for "{query}" and analyze sentiment.
Return only JSON:
{{"positive": 0-100, "neutral": 0-100, "negative": 0-100, "score": -1 to 1}}"""
}]
)
return response.choices[0].message.content
```
Detailed Sentiment Analysis
```python
def get_detailed_sentiment(topic: str, timeframe: str = "24h") -> dict:
"""Get comprehensive sentiment analysis."""
response = client.chat.completions.create(
model="grok-4-1-fast",
messages=[{
"role": "user",
"content": f"""Perform detailed sentiment analysis on X for: {topic}
Timeframe: Last {timeframe}
Return JSON:
{{
"overall_sentiment": {{
"label": "bullish/bearish/neutral",
"score": -1 to 1,
"confidence": 0 to 1
}},
"breakdown": {{
"positive": {{"percent": 0-100, "count": n}},
"negative": {{"percent": 0-100, "count": n}},
"neutral": {{"percent": 0-100, "count": n}}
}},
"themes": [
{{"theme": "...", "sentiment": "...", "frequency": n}}
],
"influencer_sentiment": [
{{"handle": "@...", "sentiment": "...", "followers": n}}
],
"trending_hashtags": ["#tag1", "#tag2"],
"sentiment_drivers": {{
"positive_factors": ["..."],
"negative_factors": ["..."]
}}
}}"""
}]
)
return response.choices[0].message.content
```
Comparative Sentiment
```python
def compare_sentiment(topics: list) -> dict:
"""Compare sentiment across multiple topics."""
topics_str = ", ".join(topics)
response = client.chat.completions.create(
model="grok-4-1-fast",
messages=[{
"role": "user",
"content": f"""Compare X sentiment for: {topics_str}
Return JSON:
{{
"comparison": [
{{
"topic": "...",
"sentiment_score": -1 to 1,
"volume": "high/medium/low",
"trend": "improving/declining/stable"
}}
],
"winner": "most positive topic",
"loser": "most negative topic",
"insights": ["..."]
}}"""
}]
)
return response.choices[0].message.content
```
Sentiment Over Time
```python
def sentiment_timeline(topic: str, periods: list) -> dict:
"""Track sentiment changes over time."""
response = client.chat.completions.create(
model="grok-4-1-fast",
messages=[{
"role": "user",
"content": f"""Analyze how sentiment for "{topic}" has changed on X.
Return JSON with sentiment for different time periods:
{{
"topic": "{topic}",
"timeline": [
{{"period": "last hour", "score": -1 to 1}},
{{"period": "last 24 hours", "score": -1 to 1}},
{{"period": "last week", "score": -1 to 1}}
],
"trend": "improving/declining/stable",
"momentum": "accelerating/decelerating/steady",
"key_events": [
{{"time": "...", "event": "...", "impact": "..."}}
]
}}"""
}]
)
return response.choices[0].message.content
```
Financial Sentiment Analysis
Stock Sentiment
```python
def stock_sentiment(ticker: str) -> dict:
"""Analyze stock sentiment with financial context."""
response = client.chat.completions.create(
model="grok-4-1-fast",
messages=[{
"role": "user",
"content": f"""Analyze X sentiment for ${ticker} stock.
Return JSON:
{{
"ticker": "{ticker}",
"sentiment": {{
"overall": "bullish/bearish/neutral",
"score": -1 to 1,
"strength": "strong/moderate/weak"
}},
"trading_signals": {{
"retail_sentiment": "...",
"smart_money_mentions": "...",
"options_chatter": "..."
}},
"catalysts_mentioned": ["earnings", "product", "macro"],
"price_predictions": {{
"bullish_targets": [...],
"bearish_targets": [...]
}},
"risk_factors": ["..."],
"recommendation": "..."
}}"""
}]
)
return response.choices[0].message.content
```
Crypto Sentiment
```python
def crypto_sentiment(coin: str) -> dict:
"""Analyze cryptocurrency sentiment."""
response = client.chat.completions.create(
model="grok-4-1-fast",
messages=[{
"role": "user",
"content": f"""Analyze X sentiment for {coin} cryptocurrency.
Return JSON:
{{
"coin": "{coin}",
"sentiment_score": -1 to 1,
"fear_greed_indicator": "extreme fear/fear/neutral/greed/extreme greed",
"whale_mentions": "high/medium/low",
"influencer_sentiment": [...],
"trending_narratives": [...],
"fud_detection": {{
"level": "high/medium/low",
"sources": [...]
}},
"fomo_detection": {{
"level": "high/medium/low",
"triggers": [...]
}}
}}"""
}]
)
return response.choices[0].message.content
```
Batch Sentiment Analysis
```python
def batch_sentiment(topics: list) -> list:
"""Analyze sentiment for multiple topics efficiently."""
topics_formatted = "\n".join([f"- {t}" for t in topics])
response = client.chat.completions.create(
model="grok-4-1-fast",
messages=[{
"role": "user",
"content": f"""Analyze X sentiment for each:
{topics_formatted}
Return JSON array:
[
{{"topic": "...", "score": -1 to 1, "label": "...", "volume": "high/med/low"}}
]"""
}]
)
return response.choices[0].message.content
```
Sentiment Alerts
```python
def check_sentiment_alert(topic: str, threshold: float = 0.5) -> dict:
"""Check if sentiment has crossed alert threshold."""
response = client.chat.completions.create(
model="grok-4-1-fast",
messages=[{
"role": "user",
"content": f"""Check X sentiment for {topic}.
Alert threshold: {threshold} (positive) or {-threshold} (negative)
Return JSON:
{{
"topic": "{topic}",
"current_score": -1 to 1,
"alert_triggered": true/false,
"alert_type": "bullish/bearish/none",
"reason": "...",
"recommended_action": "..."
}}"""
}]
)
return response.choices[0].message.content
```
Best Practices
1. Request Confidence Scores
Always ask for confidence levels to gauge reliability.
2. Specify Sample Size
Request the number of posts analyzed for context.
3. Account for Sarcasm
Grok may misinterpret sarcasm - request explicit sarcasm detection:
```python
"Note: Flag any potentially sarcastic posts separately"
```
4. Filter by Quality
Combine with handle filtering for higher-quality signals:
```python
"Focus on verified accounts and accounts with >10k followers"
```
5. Combine with Price Data
Sentiment is most valuable when combined with price action.
Limitations
| Limitation | Mitigation |
|------------|------------|
| Sarcasm detection | Request explicit flagging |
| Bot content | Ask to filter suspicious patterns |
| Historical accuracy | Focus on recent data |
| Sample size | Request volume metrics |
Related Skills
xai-x-search- X search functionalityxai-stock-sentiment- Stock-specific analysisxai-crypto-sentiment- Crypto analysisxai-financial-integration- Combine with price data
References
- [xAI Cookbook - Sentiment Analysis](https://docs.x.ai/cookbook/examples/sentiment_analysis_on_x)
- [Grok 4.1 Fast](https://x.ai/news/grok-4-1-fast/)
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