📊 News Sentiment Analysis Web App

A Flask-powered web application that analyzes sentiment in news articles and generates beautiful word clouds to visualize emotional trends in current events.

🎯 What it does

This web app takes any topic you're curious about and:

• 🔍 Fetches the latest news articles from across the web
• 🧠 Analyzes the emotional tone of each article
• 📈 Shows you whether the coverage is positive, negative, or neutral
• ☁️ Creates stunning word clouds to visualize the most common terms

Perfect for understanding public sentiment on current events, trending topics, or any subject you're researching!

✨ Features

• 🔎 Smart Topic Search - Enter any keyword and get relevant news articles
• 😊😐😞 Sentiment Classification - Automatic categorization into positive, negative, and neutral
• 📊 Visual Analytics - Beautiful charts showing sentiment distribution
• ☁️ Word Cloud Generation - Separate word clouds for each sentiment category
• 🧹 Intelligent Text Processing - Removes noise, URLs, and irrelevant content
• 🌐 Real-time Data - Fresh news articles from the News API

🛠️ Built With

• Flask - Lightweight Python web framework
• News API - Real-time news data source
• NLTK - Natural language processing toolkit
• VADER Sentiment - Lexicon-based sentiment analysis
• WordCloud - Word cloud generation
• Pandas - Data manipulation and analysis

🚀 Getting Started

Prerequisites

• Python 3.7 or higher
• A News API key (free tier available)

Installation

1. Clone the repository

   git clone https://github.com/alexquilis1/news-sentiment-analyzer.git
   cd news-sentiment-analyzer

2. Install dependencies

   pip install -r requirements.txt

3. Set up your API key

   Create a config.py file in the project root:

   API_KEY = "your_news_api_key_here"

4. Download NLTK data (first time only)

   import nltk
   nltk.download('punkt')
   nltk.download('stopwords')
   nltk.download('wordnet')

Usage

1. Start the application

   python app.py

2. Open your browser and navigate to http://localhost:5000

3. Enter a topic (e.g., "climate change", "artificial intelligence", "olympics")

4. Explore the results - View sentiment distribution and word clouds!

🎓 What I Learned

This was one of my early Python projects where I explored:

• Web development with Flask
• Natural language processing techniques
• Data visualization and web scraping
• API integration and data manipulation
• Text preprocessing and sentiment analysis algorithms

🤝 Contributing

Feel free to dive in! Open an issue or submit a pull request.

📄 License

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

👨‍💻 Author

Alex Quilis - Initial work - alexquilis1

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