xAI Sentiment Analysis

Real-time sentiment analysis on Twitter/X content using Grok's native integration and built-in NLP capabilities.

Quick Start

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

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

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

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

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

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

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

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

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:

"Note: Flag any potentially sarcastic posts separately"

4. Filter by Quality

Combine with handle filtering for higher-quality signals:

"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 functionality
• xai-stock-sentiment - Stock-specific analysis
• xai-crypto-sentiment - Crypto analysis
• xai-financial-integration - Combine with price data

References

• xAI Cookbook - Sentiment Analysis
• Grok 4.1 Fast