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        <title>Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction</title>
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        <namePart type="given">Zeyu</namePart>
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    <name type="personal">
        <namePart type="given">Reema</namePart>
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    <abstract>Compliments and concerns in reviews are valuable for understanding users’ shopping interests and their opinions with respect to specific aspects of certain items. Existing review-based recommenders favor large and complex language encoders that can only learn latent and uninterpretable text representations. They lack explicit user-attention and item-property modeling, which however could provide valuable information beyond the ability to recommend items. Therefore, we propose a tightly coupled two-stage approach, including an Aspect-Sentiment Pair Extractor (ASPE) and an Attention-Property-aware Rating Estimator (APRE). Unsupervised ASPE mines Aspect-Sentiment pairs (AS-pairs) and APRE predicts ratings using AS-pairs as concrete aspect-level evidences. Extensive experiments on seven real-world Amazon Review Datasets demonstrate that ASPE can effectively extract AS-pairs which enable APRE to deliver superior accuracy over the leading baselines.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.66</identifier>
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        <date>2021-nov</date>
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%0 Conference Proceedings
%T Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction
%A Li, Zeyu
%A Cheng, Wei
%A Kshetramade, Reema
%A Houser, John
%A Chen, Haifeng
%A Wang, Wei
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 nov
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F li-etal-2021-recommend-reason
%X Compliments and concerns in reviews are valuable for understanding users’ shopping interests and their opinions with respect to specific aspects of certain items. Existing review-based recommenders favor large and complex language encoders that can only learn latent and uninterpretable text representations. They lack explicit user-attention and item-property modeling, which however could provide valuable information beyond the ability to recommend items. Therefore, we propose a tightly coupled two-stage approach, including an Aspect-Sentiment Pair Extractor (ASPE) and an Attention-Property-aware Rating Estimator (APRE). Unsupervised ASPE mines Aspect-Sentiment pairs (AS-pairs) and APRE predicts ratings using AS-pairs as concrete aspect-level evidences. Extensive experiments on seven real-world Amazon Review Datasets demonstrate that ASPE can effectively extract AS-pairs which enable APRE to deliver superior accuracy over the leading baselines.
%R 10.18653/v1/2021.findings-emnlp.66
%U https://aclanthology.org/2021.findings-emnlp.66
%U https://doi.org/10.18653/v1/2021.findings-emnlp.66
%P 763-778

Markdown (Informal)

[Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction](https://aclanthology.org/2021.findings-emnlp.66) (Li et al., Findings 2021)

• Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction (Li et al., Findings 2021)

ACL

• Zeyu Li, Wei Cheng, Reema Kshetramade, John Houser, Haifeng Chen, and Wei Wang. 2021. Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 763–778, Punta Cana, Dominican Republic. Association for Computational Linguistics.