Amazon-Handmade-Reviews-23-Sentiment-and-NER

Overview

This project compares the performance of two NLP systems, AWS Comprehend and SpaCy, on a subset of the Amazon Handmade reviews dataset. The tasks include sentiment analysis and named entity recognition (NER).

Data

The dataset used is a subset of the Amazon Reviews 2023 dataset collected by Professor Julian McAuley and his team at UCSD, containing 664,162 reviews of Amazon Handmade items. Source: Amazon Reviews 2023 Subset: 664,162 reviews of Amazon Handmade items

NLP Tools

AWS Comprehend: Amazon's proprietary NLP service

SpaCy: Open-source NLP library eng_spacysentiment: SpaCy-based sentiment analysis extension

Key Findings

• Sentiment Analysis: AWS Comprehend generally captures sentiment nuances better, especially in short reviews. SpaCy tends to struggle and misclassify short (one-two words) reviews in particular.

• NER: SpaCy provides more detailed entity categorization (e.g., distinguishing between CARDINAL, ORDINAL, MONEY), whereas AWS Comprehend uses broader generalizations (e.g., QUANTITY for any numeric expressions).

Key Visualizations

Contact

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