trendscout 0.2.2
AI-Powered Market Trend Analysis & Sentinel
Requires Python: >=3.8

# 📈 TrendScout: AI Market Intelligence & Sentiment Analysis

[![PyPI version](https://img.shields.io/pypi/v/trendscout.svg)](https://pypi.org/project/trendscout/)
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)

**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

```bash
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.

```python
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
```text
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.

```python
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
```text
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.