/ReviewSentinel

A machine learning-driven project designed to detect and flag computer-generated or fake product reviews, ensuring authentic customer feedback and maintaining the integrity of online reviews.

Primary LanguageRich Text FormatMIT LicenseMIT

ReviewSentinel

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What is Product Zen 360?

An ambitious project where we use AI/ML to provide a 360-degree analytical view on the existing product such as:

[T1] what’s going right [T2] what needs improvement [T3] market competition [T4] inventory management

What Review Sentinel does?

Review Sentinel is one of the many modules of the project "Product Zen 360". This is an end-to-end machine learning-driven project designed to detect and flag computer-generated fake product reviews, ensuring authentic customer feedback and maintaining the integrity of online reviews.

Task List

  • Data Profiling(EDA) | Preliminary Analysis
  • Data Preprocessing | Baseline Predictions
  • Experiment Tracking and Model Registry
  • Workflow Orchestration
  • Model Deployment
  • Model Monitoring
  • Add reproducibility instructions | Documentation
  • Containerize development and deployment with localstack in docker
  • Add unit and integration tests
  • Use linters/formatters
  • Add makefile
  • Use pre-commit hooks
  • CI/CD pipeline
  • Webscraping for new reviews
  • LLMs based Review generation

Resources

Useful Links

Dataset Used : https://osf.io/tyue9/