# DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction

arXiv: https://arxiv.org/abs/1906.01794 · HTML: https://mlsanity.com/paper/1906.01794/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 17 of 17)

- [Aspect Sentiment Quad Prediction as Paraphrase Generation](https://mlsanity.com/paper/2110.00796/) (2021)
- [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022)
- [MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction](https://mlsanity.com/paper/2305.12627/) (2023)
- [Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2103.07665/) (2021)
- [Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2106.03806/) (2021)
- [Iterative Data Generation with Large Language Models for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2407.00341/) (2024)
- [Exploring Conditional Text Generation for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2110.02334/) (2021)
- [A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis](https://mlsanity.com/paper/2208.11283/) (2022)
- [Instruct-DeBERTa: A Hybrid Approach for Aspect-based Sentiment Analysis on Textual Reviews](https://mlsanity.com/paper/2408.13202/) (2024)
- [PGSO: Prompt-based Generative Sequence Optimization Network for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2412.00763/) (2024)
- [Syntax-Guided Domain Adaptation for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2211.05457/) (2022)
- [E2TP: Element to Tuple Prompting Improves Aspect Sentiment Tuple Prediction](https://mlsanity.com/paper/2405.06454/) (2024)
- [FCKT: Fine-Grained Cross-Task Knowledge Transfer with Semantic Contrastive Learning for Targeted Sentiment Analysis](https://mlsanity.com/paper/2505.21040/) (2025)
- [Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction](https://mlsanity.com/paper/2109.03821/) (2021)
- [Entity-Aspect-Opinion-Sentiment Quadruple Extraction for Fine-grained Sentiment Analysis](https://mlsanity.com/paper/2311.16678/) (2023)
- [Syntax-Informed Interactive Model for Comprehensive Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2312.03739/) (2023)
- [Aspect and Opinion Term Extraction Using Graph Attention Network](https://mlsanity.com/paper/2404.19260/) (2024)

## Abstract (arXiv)

This paper focuses on two related subtasks of aspect-based sentiment analysis, namely aspect term extraction and aspect sentiment classification, which we call aspect term-polarity co-extraction. The former task is to extract aspects of a product or service from an opinion document, and the latter is to identify the polarity expressed in the document about these extracted aspects. Most existing algorithms address them as two separate tasks and solve them one by one, or only perform one task, which can be complicated for real applications. In this paper, we treat these two tasks as two sequence labeling problems and propose a novel Dual crOss-sharEd RNN framework (DOER) to generate all aspect term-polarity pairs of the input sentence simultaneously. Specifically, DOER involves a dual recurrent neural network to extract the respective representation of each task, and a cross-shared unit to consider the relationship between them. Experimental results demonstrate that the proposed framework outperforms state-of-the-art baselines on three benchmark datasets.