Amazon Elastic Review Review (Amazon ER2)

Welcome to Amazon Elastic Review Review, an advanced review analyzer tailored for Amazon sellers. Our tool leverages cutting-edge technologies to identify key issues from customer feedback, cluster them for better understanding, and prioritize them based on their impact on your business.

Note: this project is not affiliated with Amazon, and was built as a solution for a LauzHack 2023 challenge prepared by AWS.

Key Features:

• Issue Identification: Extracts critical insights from reviews using advanced NLP models.
• Clustering: Groups similar issues using the OPTICS clustering algorithm for a clearer overview.
• Prioritization: Prioritizes issues based on their frequency and impact on customer satisfaction.

How to run

You can run this demo locally only:

1. Install NPM and Python 3

2. Clone the repository

git clone https://github.com/paulmis/lauzhack23.git

3. Install all dependencies. For frontend:

cd frontend
npm i

For backend:

cd backend
python3 -m pip install -r requirements.txt

4. Provide credentials for an account with Amazon Bedrock's Claude v2 LLM enabled.

export AWS_ACCESS_KEY_ID=
export AWS_SECRET_ACCESS_KEY=
export AWS_SESSION_TOKEN=

5. Run the backend

cd backend
python3 run.py

If successful, you should see:

Create new client
  Using region: us-east-1
boto3 Bedrock client successfully created!
bedrock-runtime(https://bedrock-runtime.us-east-1.amazonaws.com)
 * Serving Flask app 'api'
 * Debug mode: on
WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
 * Running on http://127.0.0.1:5000

6. Run the populate_database.py file. This should populate the database with the provided CSV file. See section below on how to provide the data.

7. Run the frontend

cd frontend
npm run dev

If successful, you should see:

> my-app@0.1.0 dev
> next dev

   ▲ Next.js 14.0.3
   - Local:        http://localhost:3000

 ✓ Ready in 3.3s

8. Access localhost:3000 to see the user interface.

Provide your own dataset

To integrate your own dataset with Amazon Elastic Review Review, follow these steps:

1. Prepare your dataset in CSV format. The dataset should adhere to the following column structure:

   Product Name,Brand Name,Price,Rating,Reviews,Review Votes
   

   • Product Name: The name of the product.
   • Brand Name: The name of the brand associated with the product.
   • Price: The price of the product.
   • Rating: The rating out of 5 of the review.
   • Reviews: The text of the customer review.
   • Review Votes: The number of votes/likes the review received.

2. Locate the populate_database.py file within the project's directory.

3. Modify the CSV_FILE path variable in populate_database.py to point to your CSV file. For instance:

   CSV_FILE = "path/to/your/dataset.csv"

   Replace path/to/your/dataset.csv with the actual file path of your dataset.

4. After updating the CSV_FILE path, the application will use your dataset for its analysis process.

5. Rerunning the populate_database.py should now add the new dataset to the database.

Note: If the script has been previously ran deleting the old database would be needed.

Technologies Used

• Amazon Bedrock: Robust infrastructure for scalable data processing.
• Claude V2 LLM: State-of-the-art language model for natural language understanding.
• Titan Embeddings: Powerful feature extraction for accurate text analysis.
• Stable Diffusion Image Generation: Generates visual representations of data trends and clusters.
• OPTICS Clustering: Advanced clustering algorithm for nuanced data segmentation.