Product Launch Analyzer 📱

A comprehensive sentiment analysis platform for monitoring social media reactions to product launches. This project demonstrates end-to-end data engineering, natural language processing, and business intelligence capabilities through an interactive web dashboard.

🎯 Project Overview

Product Launch Analyzer helps companies understand customer sentiment around new product releases by:

• Collecting social media data from Twitter API with intelligent fallback
• Processing text data using advanced NLP techniques
• Analyzing sentiment patterns and trends over time
• Visualizing insights through interactive charts and dashboards
• Storing processed data for historical analysis

Business Value

• Real-time Monitoring: Track customer reactions as they happen
• Competitive Analysis: Compare sentiment across different products
• Strategic Insights: Data-driven decisions for marketing and product teams
• Trend Identification: Spot emerging issues or positive momentum early

🏗️ Technical Architecture

Data Collection → Processing → Analysis → Visualization → Storage
     ↓              ↓          ↓           ↓            ↓
  Twitter API   Text Cleaning  Sentiment  Interactive   SQLite
  + Fallback    + Validation   Analysis   Dashboard    Database

Core Components

1. Data Ingestion Layer (tweet_collector.py)
2. Processing Engine (data_processor.py, sentiment_analyzer.py)
3. Storage Layer (database.py)
4. Visualization Layer (charts.py, app.py)
5. Orchestration Layer (pipeline.py)

📊 Usage Examples

Basic Pipeline Execution

# Collect and analyze data for a single product
from src.pipeline import run_pipeline_for_product

result = run_pipeline_for_product("iPhone 15", tweet_count=50)
print(f"Processed {result['tweets_stored']} tweets")

Custom Sentiment Analysis

from src.sentiment_analyzer import analyze_sentiment

# Analyze custom text
text = "The new iPhone camera is absolutely incredible!"
score = analyze_sentiment(text)
print(f"Sentiment score: {score:.3f}")  # Output: 0.625 (positive)

Data Filtering and Analysis

from src.utils import filter_tweets_by_date, calculate_metrics
from datetime import date, timedelta

# Filter tweets by date range
start_date = date.today() - timedelta(days=7)
end_date = date.today()
recent_tweets = filter_tweets_by_date(tweets, "iPhone 15", [start_date, end_date])

# Calculate metrics
metrics = calculate_metrics(recent_tweets)
print(f"Average sentiment: {metrics['avg_sentiment']:.3f}")
print(f"Positive tweets: {metrics['positive_tweets']}")

🔧 Key Features

1. Intelligent Data Collection

• Primary: Twitter API integration with authentication
• Fallback: Realistic sample data generation
• Rate limiting and error handling
• Automatic retry mechanisms

2. Text Processing

def clean_text(text):
    # Remove URLs, mentions, hashtags
    text = re.sub(r'http\S+|@\w+|#\w+', '', text)
    # Normalize whitespace
    text = ' '.join(text.split())
    return text.strip()

3. Sentiment Analysis Pipeline

• Text preprocessing and cleaning
• Polarity scoring (-1 to +1 scale)
• Confidence measurement
• Batch processing capabilities

4. Interactive Dashboard

• Real-time filtering by product and date
• Multiple visualization types
• Export functionality (CSV download)
• Responsive design for different screen sizes

5. Data Persistence

• SQLite database for local storage
• Automatic schema management
• Data validation and integrity checks
• Backup and recovery utilities

📈 Sample Outputs

Dashboard Metrics

📊 iPhone 15 Analysis (Last 7 Days)
├── Total Tweets: 1,247
├── Average Sentiment: 0.342 (Positive)
├── Positive %: 65.2%
└── Peak Day: 2024-01-15 (89 tweets)

Sentiment Analysis Results

{
    "product": "iPhone 15",
    "total_tweets": 1247,
    "sentiment_distribution": {
        "positive": 813,    # 65.2%
        "neutral": 298,     # 23.9%
        "negative": 136     # 10.9%
    },
    "average_sentiment": 0.342,
    "peak_engagement_day": "2024-01-15"
}

🧪 Testing

Run Unit Tests

# Run all tests
python tests/test_basic.py


### Test Coverage
- Sentiment analysis accuracy
- Database operations
- Data processing functions
- Utility function validation
- Error handling scenarios

## 🚀 Deployment Options

### Local Development
```bash
streamlit run src/app.py --server.port 8501

Production Deployment

# Using Streamlit Cloud
# 1. Push to GitHub
# 2. Connect to Streamlit Cloud
# 3. Deploy automatically

## 🔍 Monitoring & Debugging

### API Status Check
```bash
python utility_scripts/quick_api_check.py

Database Diagnostics

python utility_scripts/debug.py

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