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https://github.com/vishwas-chakilam/twitter-sentiment-analysis
Twitter Sentiment Analysis is a Python project that analyzes the sentiment of tweets based on a user-defined keyword. It uses Tweepy to fetch tweets from the Twitter API and TextBlob for sentiment analysis. The application features a user-friendly GUI with Tkinter, displaying tweet sentiment as positive, negative, or neutral.
https://github.com/vishwas-chakilam/twitter-sentiment-analysis
Last synced: 1 day ago
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Twitter Sentiment Analysis is a Python project that analyzes the sentiment of tweets based on a user-defined keyword. It uses Tweepy to fetch tweets from the Twitter API and TextBlob for sentiment analysis. The application features a user-friendly GUI with Tkinter, displaying tweet sentiment as positive, negative, or neutral.
- Host: GitHub
- URL: https://github.com/vishwas-chakilam/twitter-sentiment-analysis
- Owner: Vishwas-Chakilam
- License: mit
- Created: 2024-08-24T17:12:53.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-08-24T17:20:47.000Z (3 months ago)
- Last Synced: 2024-08-24T18:42:34.415Z (3 months ago)
- Language: Python
- Size: 11.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Twitter Sentiment Analysis 🎉
Unlock the power of social media insights with **Twitter Sentiment Analysis**, a dynamic Python application that lets you dive into the world of Twitter to uncover what people are feeling! 🌎
## Table of Contents
- [Features](#features)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Usage](#usage)
- [License](#license)
- [Contributing](#contributing)
## 🚀 Features
- **Real-time Sentiment Analysis**: Instantly analyze tweets in real-time based on any keyword you choose and see whether people are feeling positive, negative, or neutral.
- **Interactive GUI**: Enjoy a sleek and easy-to-use graphical interface built with Tkinter. No command line required—just click and go!
- **Detailed Feedback**: Not only does it show you the sentiment, but it also displays the polarity score, giving you a deeper understanding of the public mood.
## 🛠️ Prerequisites
Get started quickly by ensuring you have the following:
1. **Python 3.x**: Make sure you have Python installed.
2. **Twitter Developer Account**: Grab your API keys by creating a Twitter Developer account.
3. **Python Libraries**: Install the necessary libraries with a single command.
4. **Tkinter**: Ensure that Tkinter is installed on your system. Tkinter is usually included with Python installations. If not, install it manually:
- **Windows**: Tkinter is included with Python.
- **macOS**: It is included with Python. If needed, reinstall Python from the official Python website.
- **Linux**: Install Tkinter using your package manager, for example:
```bash
sudo apt-get install python3-tk
```
## 📦 Installation
Get up and running in minutes:
1. **Clone the Repository**:
```bash
git clone https://github.com/your-username/twitter-sentiment-analysis.git
cd twitter-sentiment-analysis
```
2. **Install Required Python Libraries**:
Install the required libraries using `pip`:
```bash
pip install tweepy textblob
```
3. **Configure Twitter API Keys**:
Insert your Twitter API credentials into the script:
```python
consumer_key = 'YOUR_CONSUMER_KEY'
consumer_secret = 'YOUR_CONSUMER_SECRET'
access_token = 'YOUR_ACCESS_TOKEN'
access_token_secret = 'YOUR_ACCESS_TOKEN_SECRET'
```
## 🎉 Usage
1. **Run the Application**:
Fire up the app with:
```bash
python twitter_sentiment_analysis.py
```
2. **Analyze and Explore**:
- Enter a keyword or hashtag in the input field.
- Click **Analyze** and watch the magic happen as the app fetches and analyzes tweets.
- Explore the results in the scrollable text area to see what the world is saying!
## 📜 License
This project is open-source and available under the MIT License. Check out the [LICENSE](LICENSE) file for more details.
## 🤝 Contributing
Got an idea to make this project even better? Contributions are welcome! Whether it's a bug fix, feature enhancement, or just improving documentation, we’d love to have your input.
To contribute:
1. Fork the repository.
2. Create a new branch (`git checkout -b feature-branch`).
3. Commit your changes (`git commit -m 'Add some feature'`).
4. Push to the branch (`git push origin feature-branch`).
5. Open a Pull Request.
Jump in, get your hands dirty, and let’s make something amazing together! 🎊
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