🧠 Tweet Sentiment Classifier

This project analyzes the sentiment of tweets using TextBlob and provides:

• ✅ A Python backend script that reads tweets from CSV files, performs sentiment analysis, and visualizes results.
• 🌐 A Streamlit-based frontend app for real-time tweet sentiment prediction with color-coded word highlights.

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🔍 Features

• Perform sentiment analysis (Positive, Negative, Neutral) on tweet data.
• Visualize:
  • 📊 Sentiment distribution using Seaborn bar charts.
  • ☁️ Most frequent words using a WordCloud.
• Input new tweets through a Streamlit UI for instant sentiment feedback.
• Export predicted sentiments from test data to a CSV file.

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🛠️ Technologies Used

• Python 3
• Pandas
• TextBlob
• Matplotlib & Seaborn
• WordCloud
• Streamlit

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📁 Project Structure

project/ ├── train.csv # Training tweets ├── test.csv # Test tweets for prediction ├── sentiment_analysis.py # Backend script (CSV-based sentiment analysis + visualization) ├── app.py # Streamlit frontend app ├── test_predictions.csv # Output predictions from test.csv └── README.md # Project documentation

📥 Setup Instructions

1. 🔽 Clone the project or download the files.

2. 💻 Install the required libraries:

pip install pandas matplotlib seaborn textblob wordcloud streamlit
python -m textblob.download_corpora

▶️ Running the Project
🔹 Run Backend Script:

 ``` python sentiment_analysis.py ```

Cleans and analyzes train.csv & test.csv
Predicts sentiment
Generates bar chart + word cloud
Saves results to test_predictions.csv

🔹 Run Streamlit Web App:
``` streamlit run app.py ``` 

Input a tweet
See polarity score and emoji feedback
Highlights each word:

🟩 Green = Positive

🟥 Red = Negative

⚪ Neutral = No color

📊 Example Output
Tweet:
I absolutely love this product. It's brilliant!

Result:
😊 Positive (Score: 0.6)
I absolutely :green[love] this product. It's :green[brilliant]!

📌 Notes

Sentiment is based on:
``` TextBlob(text).sentiment.polarity > 0 → Positive < 0 → Negative = 0 → Neutral ``` 

Missing data is dropped automatically

🚀 Future Improvements
Replace TextBlob with advanced models (VADER, BERT)
Host frontend on Streamlit Cloud
Add more NLP features like named entity recognition or emotion tagging

## 👩‍💻 Author
Created as a mini project to explore NLP and sentiment analysis.