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The tasks include sentiment analysis and named entity recognition (NER).\n\n## Data\nThe 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.\nSource: [Amazon Reviews 2023](https://amazon-reviews-2023.github.io/)\nSubset: 664,162 reviews of Amazon Handmade items\n\n## NLP Tools\n**AWS Comprehend**: Amazon's proprietary NLP service\n\n**SpaCy**: Open-source NLP library\neng_spacysentiment: SpaCy-based sentiment analysis extension \n\n## Key Findings\n- **Sentiment Analysis**:\nAWS Comprehend generally captures sentiment nuances better, especially in short reviews.\nSpaCy tends to struggle and misclassify short (one-two words) reviews in particular. \n\n- **NER**: \nSpaCy 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).\n\n## Key Visualizations\n\n![AWS and SpaCy Sentiment Breakdown](AWS_and_SpaCy_sentiment.png)\n![AWS and SpaCy Named Entity Recognition](AWS_and_SpaCy_wordcloud.png) \n\n## Contact\nFor any questions, please [get in touch!](mailto:tanya_kuznetsova@icloud.com).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftanyakuznetsova%2FAmazon-Handmade-NER","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftanyakuznetsova%2FAmazon-Handmade-NER","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftanyakuznetsova%2FAmazon-Handmade-NER/lists"}