About This Architecture

Aspect-Based Sentiment Analysis (ABSA) pipeline transforms raw customer reviews into ranked product insights through six sequential NLP and ML stages. Customer reviews flow through text preprocessing, aspect extraction using NER and dependency parsing, sentiment classification per aspect, and weighted scoring to produce ranked product lists with aspect-level sentiment breakdowns. This workflow demonstrates best practices for converting unstructured review data into actionable business intelligence for e-commerce platforms. Fork this diagram on Diagrams.so to customize preprocessing techniques, sentiment models, or ranking algorithms for your product catalog. The pipeline's modular design enables easy integration of domain-specific lexicons or transfer learning models like BERT for improved aspect detection.

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How do you build an aspect-based sentiment analysis pipeline to rank products from customer reviews?

This ABSA workflow extracts aspects from reviews using NER and dependency parsing, classifies sentiment polarity per aspect, then aggregates weighted scores to rank products. The six-stage pipeline—from text preprocessing through final ranking—enables e-commerce platforms to surface product strengths and weaknesses at the aspect level.