Papers with Amazon datasets
Is It Dish Washer Safe? Automatically Answering “Yes/No” Questions Using Customer Reviews (N19-3)
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| Challenge: | Using Amazon reviews, we find that the answer to a question is only in 45% of cases. |
| Approach: | They combine Amazon reviews with consumer reviews and manually analyse 400 questions from four domains to find that reviews directly contain the answer to the question . they then compare QA systems that use reviews in addition to the questions to see if they can be useful for other question types. |
| Outcome: | The proposed system outperforms the chance baseline but not by a large margin. |
Self-Supervised Multimodal Opinion Summarization (2021.acl-long)
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| Challenge: | Existing methods for opinion summarization use text data, but non-text data are less abundant. |
| Approach: | They propose a self-supervised opinion summarization framework that uses non-text data to generate a summary from multiple reviews. |
| Outcome: | The proposed framework is superior to existing methods on Yelp and Amazon datasets. |
Specificity-Driven Cascading Approach for Unsupervised Sentiment Modification (D19-1)
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| Challenge: | Existing methods for unsupervised sentiment modification lack specific information in text generated without parallel data . specificity-driven cascading approach can improve specificity of generated text and content preservation . |
| Approach: | They propose a specificity-driven cascading approach for unsupervised sentiment modification . the method performs target sentiment addition and content reconstruction independently . |
| Outcome: | The proposed method outperforms competitive systems by a large margin on Yelp and Amazon datasets. |