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Alicorn Infotech Projects

SentimentLens: ML-Powered Customer Feedback Analyser

Machine Learning

Python scikit-learn Hugging Face Transformers (BERT) FastAPI PostgreSQL Apache Kafka React Recharts Docker GitHub Actions (CI/CD)

Project Overview

A machine learning pipeline that ingests customer feedback from multiple channels, classifies sentiment, extracts topics, and surfaces actionable insights through a real-time dashboard — replacing manual feedback triage.

Client: Concept Build — RetailEdge Group

Duration: 6 weeks

The Challenge

The client received thousands of customer feedback submissions weekly across email, in-app surveys, and review platforms. Manual triage by a small team meant critical issues were often spotted days late. They needed automated, real-time insight surfacing.

Our Solution

Built a Python ingestion service that consumes feedback from multiple sources via Apache Kafka. A fine-tuned BERT model (Hugging Face Transformers) classifies sentiment (positive / neutral / negative) and a secondary scikit-learn topic classifier groups feedback into product areas. FastAPI exposes a REST endpoint for the React dashboard, which surfaces trending topics, sentiment shifts, and volume anomalies in real time using Recharts. The full ML pipeline and API are containerised with Docker; GitHub Actions runs model evaluation tests and deploys on every merge to main.

Results

  • Feedback triage time reduced from days to near real-time
  • BERT sentiment model achieved 91% accuracy on the client's domain-specific corpus
  • Trending negative topics surfaced to product teams within minutes of volume spike
  • CI/CD pipeline includes automated model evaluation — accuracy regressions block deployment

Client Testimonial

"Alicorn built the LiteCloud practice management platform for us — it handles our entire client workflow, task tracking and reporting. It has been running in production for over 8 months and the team has been responsive throughout. The co-founders are directly involved, which makes a real difference."

Lee Phillips

Digital Data Lead, Twinings Ovaltine

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