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https://github.com/tarunsha009/sentiment-analysis-api
Sentiment Analysis API: A Flask-based API to classify text sentiment as Positive, Neutral, or Negative using machine learning.
https://github.com/tarunsha009/sentiment-analysis-api
api flask genai machine-learning ml python sentiment-analysis
Last synced: about 2 months ago
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Sentiment Analysis API: A Flask-based API to classify text sentiment as Positive, Neutral, or Negative using machine learning.
- Host: GitHub
- URL: https://github.com/tarunsha009/sentiment-analysis-api
- Owner: tarunsha009
- Created: 2024-11-07T03:24:52.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-07T03:43:15.000Z (about 2 months ago)
- Last Synced: 2024-11-07T04:29:14.761Z (about 2 months ago)
- Topics: api, flask, genai, machine-learning, ml, python, sentiment-analysis
- Language: Python
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Sentiment Analysis API
This project is a **Sentiment Analysis API** built with Flask and Flask-RESTx. It classifies input text into **Positive**, **Neutral**, or **Negative** sentiment categories, using a trained machine learning model.
---
## Table of Contents
- [Project Overview](#project-overview)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Project Structure](#project-structure)
- [Model and API Details](#model-and-api-details)
- [Future Improvements](#future-improvements)
- [Contributing](#contributing)
- [License](#license)
---
## Project Overview
The API is designed to classify text-based inputs by sentiment, helping users analyze the tone of sentences or reviews. It leverages a Logistic Regression model with TF-IDF vectorization for efficient text classification.
---
## Features
- **Sentiment Classification**: Predicts Positive, Neutral, or Negative sentiment for input text.
- **Confidence Score**: Returns the model's confidence level for each prediction.
- **REST API**: Built with Flask and Flask-RESTx, with automatic API documentation.
- **Error Handling**: Provides clear error messages for invalid inputs.
---
## Installation
### 1. Clone the Repository
```bash
git clone https://github.com/tarunsha009/sentiment-analysis-api
cd sentiment-analysis-api
```
### 2. Create a Virtual Environment
```bash
python3 -m venv venv
source venv/bin/activate # On Windows use: venv\Scripts\activate
```
### 3. Install Dependencies
```bash
pip install -r requirements.txt
```
### 4. Train the Model
Run `train_model.py` to preprocess data, train the model, and save it as `text_classifier_model.joblib`.
```bash
python train_model.py
```
### 5. Run the API
Start the Flask API server.
```bash
python app.py
```
---
## Usage
To classify the sentiment of a sentence, send a POST request to `/predict` with the text you want to analyze.
**Example Request**:
```bash
curl -X POST http://127.0.0.1:5000/predict -H "Content-Type: application/json" -d '{"text": "The product is amazing!"}'
```
**Example Response**:
```json
{
"text": "The product is amazing!",
"predicted_sentiment": "Positive",
"confidence": 0.82
}
```
---
## Project Structure
```
sentiment-analysis-api/
├── app.py # Flask API with Flask-RESTx
├── preprocessor.py # Preprocessing module for text data
├── train_model.py # Model training script
├── requirements.txt # Project dependencies
└── README.md # Project documentation
```
---
## Model and API Details
- **Model**: Logistic Regression with TF-IDF vectorization (bigrams)
- **Training Data**: Custom dataset with labeled Positive, Neutral, and Negative examples
- **Confidence Threshold**: Optional threshold set to avoid low-confidence predictions (configurable in `app.py`)
---
## Future Improvements
- **Expand Training Data**: Incorporate larger datasets for improved accuracy.
- **Advanced Models**: Experiment with transformer-based models for nuanced sentiment analysis.
- **Deployment**: Deploy the API on cloud services like AWS, GCP, or Azure.
---
## Contributing
Contributions are welcome! Feel free to submit pull requests or open issues to improve the project.
---
## License
This project is licensed under the MIT License - see the LICENSE file for details.
---