Sentiment Analysis Web App

A Streamlit-based web application that performs sentiment analysis on user-provided text or news articles. It supports multilingual text analysis by using Google Translate API for automatic translation.

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🚀 Features

✅ Sentiment Analysis using VADER (NLTK's SentimentIntensityAnalyzer)
✅ Supports Multiple Languages via Google Translate API
✅ Two Modes:

• Text Analysis: Users can enter any text for sentiment analysis.
• News Article Analysis: Users can provide a URL, and the app extracts and analyzes the sentiment of the summary.
  ✅ Graphical Visualization of sentiment scores using Matplotlib
  ✅ User-friendly UI built with Streamlit

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📌 Installation

1️⃣ Clone the Repository

git clone https://github.com/your-username/sentiment-analysis-app.git
cd sentiment-analysis-app

2️⃣ Create a Virtual Environment (Optional, but Recommended)

python3 -m venv venv
source venv/bin/activate  # For macOS/Linux
venv\Scripts\activate     # For Windows

3️⃣ Install Dependencies

pip3 install -r requirements.txt

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📜 Requirements

The app requires the following Python libraries:

pip3 install streamlit
pip3 install nltk
pip3 install matplotlib
pip3 install newspaper3k
pip3 install deep-translator

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🎯 How to Run the App

Run Locally

streamlit run sentiment_app.py

The app will open in your web browser.

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🛠️ How It Works

1️⃣ Sentiment Analysis on Text

• The user enters a text.
• The app detects the language.
• If it's not English, it gets translated.
• The app performs sentiment analysis using VADER (NLTK).
• It returns:
  • Sentiment Score (range: -1 to 1).
  • Sentiment Category (Positive, Neutral, Negative).
  • Bar Chart for visualization.

2️⃣ Sentiment Analysis on News Articles

• The user enters a news article URL.
• The app extracts the text from the article.
• The text is analyzed for sentiment.
• A graph is generated.

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📊 Sentiment Score Interpretation

Sentiment Score	Interpretation
> 0	Positive Sentiment
= 0	Neutral Sentiment
< 0	Negative Sentiment

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🏗️ Future Improvements

🔹 Add Emotion Detection (Happy, Sad, Angry, etc.)
🔹 Use Deep Learning models for sentiment analysis
🔹 Improve language translation accuracy

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📝 License

This project is open-source.

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🤝 Contributing

Feel free to fork the repository, make improvements, and submit a pull request.

Happy Coding! 🚀