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https://github.com/nishant2018/twitter_scrapping_-_sentiment_analysis
This web application performs sentiment analysis on tweets using Twitter's API.
https://github.com/nishant2018/twitter_scrapping_-_sentiment_analysis
sentiment-analysis twitter-api twitter-sentiment-analysis webapp
Last synced: about 22 hours ago
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This web application performs sentiment analysis on tweets using Twitter's API.
- Host: GitHub
- URL: https://github.com/nishant2018/twitter_scrapping_-_sentiment_analysis
- Owner: Nishant2018
- Created: 2024-08-09T22:01:40.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-09-01T17:37:34.000Z (4 months ago)
- Last Synced: 2024-11-11T09:08:58.637Z (about 2 months ago)
- Topics: sentiment-analysis, twitter-api, twitter-sentiment-analysis, webapp
- Language: HTML
- Homepage:
- Size: 489 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Twitter Sentiment Analysis Web Application
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## Overview
This web application performs sentiment analysis on tweets using Twitter's API. The application provides two primary functionalities:
1. **Sentiment Count**: Analyzes a specified number of tweets from given Twitter handles and provides a sentiment breakdown (positive or negative).
2. **Get Sentiment Only**: Analyzes the sentiment of a single user-provided sentence.
The application features an interactive interface with options to view live Twitter feeds and visualize sentiment analysis results.
## Features
- **Twitter Sentiment Count**: Fetches and analyzes tweets from specified Twitter handles.
- **Single Sentence Sentiment Analysis**: Analyzes the sentiment of a user-provided sentence.
- **Live Twitter Feed**: Displays a live feed of tweets from selected handles.
- **Visualizations**: Provides a visual representation of positive and negative sentiment counts.
- **Responsive Design**: Mobile-friendly and interactive UI with Bootstrap.
## Technologies Used
- **Backend**: Flask
- **Frontend**: HTML, CSS, JavaScript, Bootstrap
- **APIs**: Twitter API
- **Data Analysis**: Python, Sentiment Analysis Libraries
- **Deployment**: Local environment or web server
## Getting Started
### Prerequisites
- Python 3.x
- Flask
- Bootstrap
- Twitter API credentials
### Installation
1. **Clone the Repository**
```bash
git clone
cd
```
2. **Set Up the Environment**
Create and activate a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
3. **Install Dependencies**
```bash
pip install -r requirements.txt
```
4. **Configure Twitter API Credentials**
Update your credentials in the Flask application configuration:
```python
consumer_key = 'your-consumer-key'
consumer_secret = 'your-consumer-secret'
access_token = 'your-access-token'
access_token_secret = 'your-access-token-secret'
```
5. **Run the Application**
```bash
python app.py
```
Access the application in your web browser at `http://127.0.0.1:5000`.
### Usage
1. **Sentiment Count**: Enter Twitter handles and the number of tweets to analyze. Click "🔍 Analyze Tweets 🔍" to fetch and analyze tweets.
2. **Get Sentiment Only**: Enter a sentence for sentiment analysis and click "🔍 Get Sentiment 🔍".
3. **View Live Feed**: Check the live Twitter feed card for real-time updates from selected handles.
### Project Structure
- **`app.py`**: The main Flask application file that handles routes and API interactions.
- **`static/css/styles.css`**: Custom styles for the web application.
- **`templates/`**: HTML templates for rendering pages.
- **`requirements.txt`**: List of Python dependencies.
### Contributing
Contributions are welcome! Please open an issue or submit a pull request.
### License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
### Contact
For any questions or feedback, please contact [[email protected]]([email protected]).
Feel free to adjust the sections and details according to your project's specific requirements.