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

arXiv: https://arxiv.org/abs/2109.03821 · HTML: https://mlsanity.com/paper/2109.03821/

## 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.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [Relational Graph Attention Network for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2004.12362/) (2020)
- [Joint Deep Modeling of Users and Items Using Reviews for Recommendation](https://mlsanity.com/paper/1701.04783/) (2017)
- [Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised](https://mlsanity.com/paper/1808.08858/) (2018)
- [SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis](https://mlsanity.com/paper/2005.05635/) (2020)
- [Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction](https://mlsanity.com/paper/1805.04601/) (2018)
- [Multi-Pointer Co-Attention Networks for Recommendation](https://mlsanity.com/paper/1801.09251/) (2018)
- [Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision](https://mlsanity.com/paper/1907.03750/) (2019)
- [DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction](https://mlsanity.com/paper/1906.01794/) (2019)
- [Embarrassingly Simple Unsupervised Aspect Extraction](https://mlsanity.com/paper/2004.13580/) (2020)
- [Attentive Aspect Modeling for Review-aware Recommendation](https://mlsanity.com/paper/1811.04375/) (2018)
- [Lifelong Learning CRF for Supervised Aspect Extraction](https://mlsanity.com/paper/1705.00251/) (2017)
- [Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation](https://mlsanity.com/paper/2001.04346/) (2019)
- [DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2004.13816/) (2020)
- [Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding](https://mlsanity.com/paper/2010.06705/) (2020)

## 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.