E-Commerce Customer Review Insights

Overview

The E-Commerce Customer Review Insights is a tool designed to analyze and process Walmart product reviews using state-of-the-art Natural Language Processing (NLP) and Machine Learning (ML) techniques. The tool extracts valuable insights from customer feedback, including sentiment analysis, review summaries, and key metrics related to customer satisfaction and product performance.

By leveraging advanced libraries like MLflow, Langchain, and Transformers, the project offers a robust pipeline for analyzing and tracking performance, as well as utilizing cutting-edge pre-trained language models for review analysis. This tool is ideal for researchers, data scientists, and e-commerce professionals looking to analyze large-scale product reviews efficiently.

Workflow

Key Features

• Sentiment Analysis: Categorizes reviews as positive or negative using NLP models.
• Review Summaries: Generates concise summaries of individual reviews using Transformers for better readability.
• Key Metrics Tracking: Logs essential review metrics such as total reviews, verified purchases, average rating, and sentiment distributions (positive/negative).
• Performance Tracking with MLflow: Utilizes MLflow to log and track performance metrics, ensuring reproducibility and efficient experimentation.
• Automated Data Handling: Efficient data processing with Langchain, structuring and processing data for different NLP tasks.

Dataset

This project uses a Walmart product review dataset, sourced from Kaggle. The dataset includes user reviews of Walmart products, containing fields such as ratings, review text, and whether the purchase was verified. The data undergoes Exploratory Data Analysis (EDA) to uncover trends and provide insights before being processed in the analysis pipeline.

Technologies Used

MLflow

MLflow is integrated into this project for logging and managing the performance of machine learning models. It helps track:

• Model training performance (accuracy, loss, etc.)
• Hyperparameters used during experimentation
• Sentiment analysis metrics and other performance indicators
• Reproducibility of experiments and models

With MLflow, model development is transparent and version-controlled, enabling continuous improvement.

Langchain

Langchain structures and manages the natural language processing pipeline. It facilitates:

• Text summarization: Automatically generating concise summaries of reviews.
• Sentiment analysis: Classifying reviews as positive or negative.
• Data structuring: Organizing and pre-processing review data for effective downstream processing.

Langchain enhances pipeline extensibility by integrating various data sources and models, improving overall efficiency.

Transformers

Transformers by Hugging Face is a powerful library for state-of-the-art NLP models. In this project, it enables:

• Text Summarization: Summarizing lengthy reviews into digestible pieces.
• Sentiment Classification: Using pre-trained models to classify reviews as positive, negative, or neutral.

These advanced NLP models allow for accurate large-scale review analysis, delivering timely insights.

MLOps Integration

Performance Tracking with MLflow

Integrating MLflow ensures that all models' performance metrics, hyperparameters, and other relevant data are logged. This enables:

• Tracking sentiment analysis model performance.
• Comparing different model versions and configurations.
• Visualizing metrics such as accuracy, loss, and other relevant values.
• Achieving model versioning for reproducibility and consistency.

Continuous Monitoring

With MLflow, users can set up continuous monitoring to assess model performance over time. This is crucial in e-commerce, where review sentiment trends may evolve.

Dependencies

This project requires the following Python libraries:

• torch==1.13.1: For deep learning model training and inference.
• numpy==1.23.0: For numerical operations.
• pandas==2.2.3: For handling and manipulating datasets.
• transformers==4.49.0: For using pre-trained NLP models from Hugging Face.
• langchain==0.3.20: For structuring and managing NLP tasks.
• mlflow==2.20.3: For tracking machine learning model performance and experiments.
• Python 3.9.21: The required Python version to run the project.

Output

After running the analysis, the script outputs key metrics and review summaries in a structured JSON format.

Example Output:

{
    "summaries": [
        "The product is great, I love it! Highly recommend it to others.",
        "Not worth the price. I had issues with the quality right away."
    ],
    "metrics": {
        "total_reviews": 1000,
        "verified_purchases": 557,
        "avg_rating": 4.066,
        "positive_reviews": 654,
        "negative_reviews": 346
    }
}

"# GenAI-Ecom-Review-Analyzer"