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https://github.com/suraj-sedai/sentiment-analysis

This project classifies tweets from Twitter into positive, negative, or neutral categories using natural language processing (NLP) techniques.
https://github.com/suraj-sedai/sentiment-analysis

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This project classifies tweets from Twitter into positive, negative, or neutral categories using natural language processing (NLP) techniques.

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README

        

# Sentiment Analysis Project

## 📌 Description
This project performs sentiment analysis on user comments using **TextBlob**. The data is cleaned using **NLTK** before applying sentiment classification.

## 📂 Project Structure
```
Sentiment-Analysis/
├── data/
│ ├── raw_sentiment_data.csv # Store the dataset here
├── notebooks/
│ ├── sentiment_analysis.ipynb # Jupyter Notebook for analysis
├── scripts/
│ ├── preprocess.py # Text cleaning functions
│ ├── analyze.py # Sentiment analysis function
├── requirements.txt # List of dependencies
├── README.md # Project documentation
├── .gitignore # Ignore unnecessary files
```

## 🛠 Installation
1. Clone this repository:
```bash
git clone [https://github.com/Suraj-Sedai/Sentiment-Analysis.git]
```
2. Navigate to the project folder:
```bash
cd Sentiment-Analysis
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```

## 🚀 How to Use
Run the Jupyter Notebook inside `notebooks/` to see the sentiment analysis in action.

## 📊 Example Output
| Comment | Sentiment |
|---------|----------|
| I love this product! | Positive |
| This is the worst experience ever. | Negative |

## 🎯 Features
- Data Cleaning with **NLTK**
- Sentiment Analysis using **TextBlob**
- Data Visualization with **Matplotlib & Seaborn**

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