A Amazon dataset of 1,396,896 QA pairs across 21 categories, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Comprehensive Amazon product Q&A data across 21 categories. Suitable for question answering, sentiment analysis, product understanding, conversational AI. The record count covers the single-answer files (qa_*.json.gz, one question and answer per line). The package also holds the multiple-answer release used in Wan and McAuley's ICDM 2016 paper (icdm/QA_*.json.gz, 17 categories: 172,617 products, 1,025,628 questions and 4,019,744 answers, with asker and answerer IDs and helpfulness votes), and McAuley's data page (index.html). Both are written as Python literals, not strict JSON (read them with ast.literal_eval).
A Amazon dataset of 314,263 QA pairs in Electronics, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Electronics. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 184,439 QA pairs in Home & Kitchen, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Home & Kitchen. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 146,891 QA pairs in Sports & Outdoors, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Sports & Outdoors. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 101,088 QA pairs in Tools & Home Improvement, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Tools & Home Improvement. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 89,923 QA pairs in Automotive, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Automotive. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 85,865 QA pairs in Cell Phones & Accessories, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Cell Phones & Accessories. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 80,496 QA pairs in Health & Personal Care, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Health & Personal Care. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 59,595 QA pairs in Patio Lawn & Garden, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Patio Lawn & Garden. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 51,486 QA pairs in Toys & Games, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Toys & Games. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 43,608 QA pairs in Office Products, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Office Products. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 42,422 QA pairs in Beauty, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Beauty. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 36,607 QA pairs in Pet Supplies, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Pet Supplies. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 28,933 QA pairs in Baby, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Baby. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 23,322 QA pairs in Musical Instruments, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Musical Instruments. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 22,068 QA pairs in Clothing Shoes & Jewelry, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Clothing Shoes & Jewelry. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 21,262 QA pairs in Arts Crafts & Sewing, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Arts Crafts & Sewing. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 19,538 QA pairs in Grocery & Gourmet Food, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Grocery & Gourmet Food. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 13,307 QA pairs in Video Games, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Video Games. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 12,136 QA pairs in Industrial & Scientific, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Industrial & Scientific. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 10,636 QA pairs in Software, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Software. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 9,011 QA pairs in Appliances, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Appliances. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
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