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Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction

arXiv 2109.03821 ↗First posted 7 Sep 2021cs.IR cs.LGcited by 0

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Abstract (arXiv)

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 evidence. 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 47 references that are in the map. First posted on arXiv 7 Sep 2021.

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References · 18

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6 more
  1. 2020Embarrassingly Simple Unsupervised Aspect Extractioncited by 13
  2. 2018Attentive Aspect Modeling for Review-aware Recommendationcited by 12
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  4. 2019Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendationcited by 9
  5. 2020DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysiscited by 9
  6. 2020Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embeddingcited by 7

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