https://github.com/ap-dev-github/sentiment-analyzer-api
A lightweight, scalable, and serverless API for real-time sentiment analysis. Built using Flask, NLTK's SentimentIntensityAnalyzer, and deployed on AWS Lambda with the Serverless Framework, it efficiently classifies text as positive, negative, or neutral.
https://github.com/ap-dev-github/sentiment-analyzer-api
aws aws-api-gateway aws-lambda bandit cost-optimization flake8 flask isort mypy natural-language-processing nltk-library production-ready prune pytest scalable-architecture serverless
Last synced: about 2 months ago
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A lightweight, scalable, and serverless API for real-time sentiment analysis. Built using Flask, NLTK's SentimentIntensityAnalyzer, and deployed on AWS Lambda with the Serverless Framework, it efficiently classifies text as positive, negative, or neutral.
- Host: GitHub
- URL: https://github.com/ap-dev-github/sentiment-analyzer-api
- Owner: ap-dev-github
- License: mit
- Created: 2025-03-04T19:49:27.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-06-20T05:21:12.000Z (4 months ago)
- Last Synced: 2025-08-09T16:37:45.447Z (2 months ago)
- Topics: aws, aws-api-gateway, aws-lambda, bandit, cost-optimization, flake8, flask, isort, mypy, natural-language-processing, nltk-library, production-ready, prune, pytest, scalable-architecture, serverless
- Language: Python
- Homepage:
- Size: 168 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Sentiment Analyzer API
## 📝 Overview
> The **Sentiment Analyzer API** intelligently processes text input via a **path parameter**, utilizing **NLTK's SentimentIntensityAnalyzer** to extract **negative, positive, and neutral scores**. Based on these values, it determines the **dominant emotion** and classifies the **sentiment in real time**.
>Designed for **efficiency and scalability**, this API **automatically tests and deploys** any code changes to **AWS Lambda** using the **Serverless Framework**. It seamlessly **balances load** with **AWS API Gateway**, ensuring **high availability and performance** under varying traffic loads.
>With its **serverless architecture**, the API **scales down to zero cost** when idle (**for Lambda alone**). When used with **API Gateway and S3**, operational costs can **reduce by up to 98%**, making it a **lightweight, cost-effective, and highly scalable** sentiment analysis solution.
## 🎯 Use Cases
- **Review Categorization**: Automatically classifies reviews as **Positive, Negative, or Neutral**.
- **Automated Customer Support**: Negative reviews trigger **AWS SNS notifications** to notify the **QA team** and send a **custom apology email** to the customer.
- **Recommendation System**: Uses **positive reviews** to suggest the best-rated products or services.
- **Scalability & Serverless**: Designed to work **seamlessly** with **AWS Lambda**, ensuring **cost efficiency and automatic scaling**.
## 📊 Scalability Optimization & Cost Efficiency
- Uses **AWS API Gateway** for **auto-scaling** and **load balancing** under high traffic.
- **Cold Start Prevention**: Removes unnecessary dependencies after testing to ensure **fast startup times**.
- The API **scales down** to nearly **zero cost** for **up to 1 million requests/month** (including AWS services like **S3, EC2, DynamoDB**).
## ⚙️ Deployment Pipeline (CI/CD)
The API is **fully serverless** and **automatically deploys** when updates are pushed to the repository.
### 🏗 **GitHub Actions Workflow**
🔹 **Triggers:**
- 📌 Push to `main` or `dev` branches
🔹 **Deployment Jobs:**
- Sets up **Python 3.9**
- Installs dependencies
- Runs **security testing** using `run_checks.sh`
- Deploys the API to **AWS Lambda using Serverless Framework**
- Prunes old deployments, keeping only the last **two versions**
## 🔐 Security & Testing Pipeline
Each deployment runs a **test script** that performs:
- ✅ **Unit Tests** (`pytest`)
- 🔍 **Linting** (`flake8`)
- 🗂 **Import Organization** (`isort`)
- 📏 **Static Type Checking** (`mypy`)
- 🛡 **Security Scans** (`bandit`)
## 🚀 API Endpoints
### 📡 1. Analyze Sentiment
```http
GET /analyze/
```
#### Parameters:
| Parameter | Type | Description |
|-----------|--------|---------------------------|
| `text` | string | Input text for sentiment analysis |
#### Example Response:
```json
{
"positive": 0.8,
"neutral": 0.1,
"negative": 0.1,
"dominant_sentiment": "Positive"
}
```
## Conclusion
The **Sentiment Analyzer API** is a **highly scalable**, **serverless** solution designed for **real-time sentiment analysis**. It is ideal for **customer feedback management**, **recommendation systems**, and **automated customer support workflows**.
🚀 **Built with Flask | Serverless | AWS Lambda | GitHub Actions | Passion :)**
## Developer and Maintainer
# **Ayush Pandey**
[🔗 Connect on LinkedIn](https://www.linkedin.com/in/linkedap/)
📧 **Email:** [ayushpandey.cs@gmail.com](mailto:ayushpandey.cs@gmail.com)