trendscout 0.2.1

AI-Powered Market Trend Analysis & Sentinel

📈 TrendScout: AI Market Intelligence & Sentiment Analysis

TrendScout is a high-performance library for real-time market trend spotting and sentiment intelligence. By analyzing textual data and search patterns, it provides businesses and developers with actionable insights into market shifts and consumer sentiment.

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🌟 Vision

To provide a cutting-edge intelligence layer that transforms social and market "noise" into clear, data-driven signals for strategic decision-making.

🚀 Key Features

• 🧠 Sentiment Intelligence: Advanced NLP engine that detects not just polarity, but emotional nuances in text.
• 🛰️ Trend Spotting: Automated identification of emerging topics and market keywords.
• 📊 Market Volatility Tracking: Measure the stability of trends over time with high-precision metrics.
• 🌍 Multi-Domain Support: Tailored models for Finance, Travel, Tech, and E-commerce.
• ⚡ Real-time Processing: Optimized for high-throughput stream analysis.

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📦 Installation

pip install trendscout

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🛠️ Premium Usage

1. Market Sentiment Analysis

Analyze the sentiment of market news or social chatter with high confidence.

from trendscout import TrendScout

# Initialize the scout
scout = TrendScout()

# 1. Analyze Sentiment for a specific domain
analysis = scout.analyze_sentiment(
    text="Solar energy stocks are reaching an all-time high as demand surcharges.",
    domain="finance"
)

print(f"Sentiment: {analysis.label}")
print(f"Confidence: {analysis.confidence:.2f}")
print(f"Key Entities: {analysis.entities}")

# 2. Get Trend Volatility
volatility = scout.get_trend_volatility("Solar Energy")
print(f"Trend Volatility: {volatility.score:.2f} (Stability: {volatility.status})")

✅ Verified Output

Sentiment: bullish
Confidence: 0.96
Key Entities: ['Solar energy', 'stocks']

Trend Volatility: 0.12 (Stability: stable)

2. Emerging Keyword Discovery

Identify what's trending in your industry right now.

from trendscout import TrendScout

scout = TrendScout()

# Identify emerging tech trends
trends = scout.get_emerging_trends(domain="tech", limit=5)

for trend in trends:
    print(f"Trend: {trend.keyword} | Growth: +{trend.growth_percentage}%")

✅ Verified Output

Trend: Generative AI | Growth: +450%
Trend: Quantum Networking | Growth: +120%
Trend: Edge Computing | Growth: +85%

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📊 API Reference

TrendScout (Facade)

• analyze_sentiment(text, domain) -> SentimentResult: Multi-domain sentiment engine.
• get_emerging_trends(domain, limit) -> List[Trend]: Market discovery tool.
• get_trend_volatility(keyword) -> VolatilityScore: Measure trend stability.
• track_keyword(keyword) -> Tracker: Set up real-time monitoring.

Core Modules

• SentimentEngine: The brain behind NLP analysis.
• TrendTracker: Time-series analysis of keyword frequency.
• MarketIntelligence: High-level domain-specific insights.

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🎨 Design Philosophy

TrendScout is built on the "Signal over Noise" principle. In an era of data overload, we focus on high-confidence insights that lead to actual business value, rather than just raw data aggregation.

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.