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https://github.com/pranam-s/sentiment-analysis-project
A sentiment analysis project for movie reviews
https://github.com/pranam-s/sentiment-analysis-project
flask nlp python sentiment-analysis supervised-learning text-classification
Last synced: 2 months ago
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A sentiment analysis project for movie reviews
- Host: GitHub
- URL: https://github.com/pranam-s/sentiment-analysis-project
- Owner: pranam-s
- Created: 2024-09-08T14:04:11.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-09-08T14:18:44.000Z (4 months ago)
- Last Synced: 2024-10-16T13:00:07.373Z (3 months ago)
- Topics: flask, nlp, python, sentiment-analysis, supervised-learning, text-classification
- Language: Python
- Homepage:
- Size: 6.84 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
Awesome Lists containing this project
README
# Movie Review Sentiment Analysis
This project demonstrates a simple sentiment analysis model for movie reviews using Python, scikit-learn, and Flask.
## Setup
1. Clone the repository:
```
git clone https://github.com/your-username/sentiment-analysis-project.git
cd sentiment-analysis-project
```
2. Create a virtual environment and activate it:
```
python -m venv venv
source venv/bin/activate # On Windows, use `venv\Scripts\activate`
```
3. Install the required packages:
```
pip install -r requirements.txt
```
4. Generate the sample data:
```
python data_generator.py
```
5. Train the model:
```
python model_trainer.py
```
6. Run the Flask app:
```
python app.py
```
7. Open your web browser and go to `http://localhost:5000` to use the sentiment analysis tool.
## Project Structure
- `data_generator.py`: Generates sample movie reviews and sentiments.
- `model_trainer.py`: Trains the sentiment analysis model using the generated data.
- `app.py`: Flask application for serving the web interface.
- `templates/`: Contains HTML templates for the web interface.
- `static/`: Contains CSS files for styling the web interface.
- `data/`: Stores the generated movie reviews CSV file.
- `models/`: Stores the trained model and vectorizer.
## Usage
1. Enter a movie review in the text area on the home page.
2. Click "Analyze Sentiment" to get the predicted sentiment.
3. View the result and analyze another review if desired.