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https://github.com/udhaya2823/sentiment-analysis-model---deploy-using-aws-services-hugging-face-pretrained-model

🚀This project delivers a scalable and secure web application, powered by AWS services and accessible via Streamlit or Gradio. 🧠The model analyzes tweets to classify sentiments as Positive, Negative, or Neutral, based on entity-level context. Key steps include fine-tuning or leveraging pre-trained models, storing them in S3, and deploying via EC2.
https://github.com/udhaya2823/sentiment-analysis-model---deploy-using-aws-services-hugging-face-pretrained-model

aws-ec2 aws-rds aws-s3 data-science deep-learning gradio huggingface-transformers python pytorch sentiment-analysis streamlit twitter-sentiment-analysis

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🚀This project delivers a scalable and secure web application, powered by AWS services and accessible via Streamlit or Gradio. 🧠The model analyzes tweets to classify sentiments as Positive, Negative, or Neutral, based on entity-level context. Key steps include fine-tuning or leveraging pre-trained models, storing them in S3, and deploying via EC2.

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README 

        
# 🚀Sentiment Analysis Model Deployment using AWS and Streamlit/Gradio



## Table of Contents

- [Introduction](#introduction)

- [Features](#features)

- [Tech Stack](#tech-stack)

- [Data](#data)

- [Setup Instructions](#setup-instructions)

- [Project Structure](#project-structure)

- [Usage](#usage)

- [Deployment](#deployment)

- [Evaluation Metrics](#evaluation-metrics)

- [Future Enhancements](#future-enhancements)

- [License](#license)



---



## Introduction

This project focuses on deploying a **Sentiment Analysis Model** using AWS services and making it accessible through a web application built with **Streamlit** or **Gradio**. It utilizes a fine-tuned or pre-trained sentiment analysis model to classify tweets into three categories: **Positive**, **Negative**, and **Neutral**.



The deployment includes setting up AWS infrastructure, ensuring security, and enabling scalability for multiple users.



---



## Features

- Entity-level sentiment analysis for tweets.

- Web interface for user interaction (Streamlit or Gradio).

- AWS-based deployment for scalability and robustness.

- Storage of user login data using Amazon RDS.

- Secure and optimized environment configuration.



---



## Tech Stack

- **Machine Learning**: Transformers, Hugging Face models

- **Web Framework**: Streamlit or Gradio

- **AWS Services**: S3, EC2, RDS, IAM

- **Database**: Amazon RDS (PostgreSQL)

- **Programming Language**: Python

- **Version Control**: Git



---



## Data

Dataset: [Twitter Training Dataset](https://raw.githubusercontent.com/GuviMentor88/Training-Datasets/refs/heads/main/twitter_training.csv)



### Dataset Fields

1. **Tweet ID**: Unique identifier for each tweet.

2. **Entity**: The topic or entity associated with the tweet.

3. **Sentiment**: Sentiment classification (Positive, Negative, Neutral).

4. **Tweet Content**: The actual text of the tweet.



---



## Setup Instructions

### Prerequisites

1. Python 3.8+ installed.

2. AWS Account with access to S3, EC2, and RDS.

3. AWS CLI configured on your local system.

4. Git installed.



### Steps

1. Clone the repository:

   ```bash

   git clone https://github.com/your-username/sentiment-analysis-aws.git

   cd sentiment-analysis-aws

   ```

2. Install dependencies:

   ```bash

   pip install -r requirements.txt

   ```

3. Set up AWS infrastructure:

   - Launch an **EC2 instance** with appropriate IAM roles and security groups.

   - Create an **S3 bucket** and upload the fine-tuned model.

   - Configure an **RDS instance** for storing user login data.

4. Configure environment variables:

   - Add AWS credentials and database configurations in a `.env` file.

5. Deploy the web app:

   ```bash

   streamlit run app.py

   ```

   or

   ```bash

   python -m gradio app.py

   ```



---



## Project Structure

```

sentiment-analysis-aws/

├── app.py                # Main application file

├── requirements.txt      # Python dependencies

├── model/                # Fine-tuned model files

├── scripts/              # Scripts for data processing and setup

├── data/                 # Sample dataset

├── README.md             # Project documentation

└── .env                  # Environment variables (not included in repo)

```



---



## Usage

1. Open the deployed web application in a browser.

2. Enter a tweet and select an entity.

3. View the sentiment classification results.



---



## Deployment

### AWS Steps

1. Upload the fine-tuned model and `app.py` to an **S3 bucket**.

2. Launch an **EC2 instance** and configure it to access the S3 bucket.

3. Install required dependencies on the EC2 instance:

   ```bash

   sudo apt update

   sudo apt install python3-pip

   pip install -r requirements.txt

   ```

4. Set up the **Streamlit/Gradio app** to run on a public-facing port.

5. Configure a **security group** to allow inbound traffic on the application port (default: 8501).



---



## Evaluation Metrics

- **Accuracy**: Measure of correct predictions.

- **Precision**: Proportion of relevant instances among retrieved instances.

- **Recall**: Proportion of relevant instances retrieved.

- **F1-Score**: Harmonic mean of precision and recall.

- **Response Time**: Time taken to process a user query.



---



## Future Enhancements

- Integrate more AWS services like Lambda for serverless computing.

- Expand the dataset for multi-language sentiment analysis.

- Enable real-time sentiment tracking using a live Twitter API.



---



## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.