https://github.com/rrayhka/sentiment-analisis-app
Web-based sentiment analysis app using BiLSTM and Attention models for text sentiment classification.
https://github.com/rrayhka/sentiment-analisis-app
attention bilstm flask nlp sentiment-analysis
Last synced: 7 months ago
JSON representation
Web-based sentiment analysis app using BiLSTM and Attention models for text sentiment classification.
- Host: GitHub
- URL: https://github.com/rrayhka/sentiment-analisis-app
- Owner: rrayhka
- Created: 2024-06-25T10:01:02.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-15T14:18:22.000Z (about 1 year ago)
- Last Synced: 2025-01-28T02:44:15.114Z (9 months ago)
- Topics: attention, bilstm, flask, nlp, sentiment-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 6.03 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Sentiment-Analysis-App
This repository contains a web-based sentiment analysis application that allows users to input text and choose between two different models for analysis: BiLSTM and Attention. The application provides a simple and interactive interface for sentiment analysis, enabling users to quickly determine the sentiment of a given sentence.
## Table of Contents
- [About](#about)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Project Structure](#project-structure)
- [Contributing](#contributing)
- [Contact](#contact)
## About
The Sentiment-Analysis-App is designed to analyze the sentiment of text data using two state-of-the-art models: BiLSTM (Bidirectional Long Short-Term Memory) and Attention. The user can input a sentence through a web interface and select which model to use for the sentiment analysis.
## Features
- **Two Models:** Choose between BiLSTM and Attention models for sentiment analysis.
- **User-Friendly Web Interface:** Simple input form for text and model selection.
- **Real-Time Sentiment Analysis:** Displays the sentiment result immediately after submission.
## Installation
1. Clone the repository:
```bash
git clone https://github.com/rrayhka/sentiment-analisis-app.git
cd sentiment-analisis-app
```
2. Run the web application:
```bash
python app.py
```
## Usage
1. **Accessing the Web Interface:**
- After running the application, open your web browser and navigate to `http://localhost:5000`.
2. **Performing Sentiment Analysis:**
- Enter the text you wish to analyze in the input field.
- Select either the BiLSTM or Attention model from the dropdown menu.
- Click "Analyze Sentiment" to receive the sentiment result.
3. **Viewing Results:**
- The sentiment result will be displayed on the page, indicating whether the input text is positive, negative, or neutral.
## Project Structure
- `app.py`: The main Flask application file that runs the web interface.
- `models/`: Directory containing pre-trained models (BiLSTM and Attention).
- `nn.py`: Contains the neural network models and the logic for loading and predicting sentiment using BiLSTM and Attention models.
- `notebooks/`: Contains Jupyter notebooks used for model training and evaluation.
- `templates/`: HTML templates for the web interface.
- `dataset/`: Contains the dataset used for training and testing.
## Contributing
Contributions are welcome! If you have any suggestions, improvements, or bug fixes, feel free to submit a pull request or open an issue.
1. Fork the repository.
2. Create your feature branch (`git checkout -b feature/AmazingFeature`).
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`).
4. Push to the branch (`git push origin feature/AmazingFeature`).
5. Open a Pull Request.
## Contact
Akhyar - [khyar075@gmail.com](mailto:khyar075@gmail.com)