Papers with Amazon reviews
Learning Universal Authorship Representations (2021.emnlp-main)
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Rafael A. Rivera-Soto, Olivia Elizabeth Miano, Juanita Ordonez, Barry Y. Chen, Aleem Khan, Marcus Bishop, Nicholas Andrews
| Challenge: | authorship verification has traditionally relied on modeling stylometric linguistic properties . but neural methods introduce a tradeoff: they obviate the need for manual feature design . |
| Approach: | They propose to use domain-specific features to improve authorship representations . they propose to study Amazon reviews, fanfiction short stories, and Reddit comments . |
| Outcome: | The proposed methods outperform existing methods in large-scale authorship verification scenarios. |
Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews (2021.eacl-main)
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| Challenge: | Existing models for sentiment-topic extraction assume topics are grouped under discrete sentiment categories such as ‘positive’, ‘negative’ and ‘neural’. |
| Approach: | They propose a Brand-Topic Model which aims to detect brand-associated polarity-bearing topics from product reviews. |
| Outcome: | The proposed model outperforms existing models on Amazon reviews and shows that it is more coherent and unique than existing models. |
Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange (D19-1)
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| Challenge: | Existing methods to adjust semantics of text while preserving its style have not been investigated to the best of our knowledge. |
| Approach: | They propose to use masking (replacement) rate threshold as an adjustable parameter to control the amount of semantic change in the text. |
| Outcome: | The proposed pipeline outperforms baseline models on Yelp reviews, Amazon reviews, and news headlines in terms of its Semantic Text Exchange Score (STES) |
The Multilingual Amazon Reviews Corpus (2020.emnlp-main)
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| Challenge: | The corpus contains reviews in English, Japanese, German, French, Spanish, and Chinese, which were collected between 2015 and 2019 . |
| Approach: | They propose to use mean absolute error (MAE) instead of classification accuracy for this task since MAE accounts for ordinal nature of the ratings. |
| Outcome: | The proposed model uses mean absolute error (MAE) instead of classification accuracy since MAE accounts for ordinal nature of the ratings. |
Argument Mining for Review Helpfulness Prediction (2022.emnlp-main)
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| Challenge: | Argumentational features have been shown to be promising indicators of product review helpfulness, but their utility has been limited due to the lack of resources and large-scale experiments investigating their utility. |
| Approach: | They present an argumentational argumentation model that annotates 878 Amazon reviews on headphones and uses it to evaluate argument quality. |
| Outcome: | The proposed model improves the state-of-the-art model under text-only and text-and-image settings. |