---
title: "Relational Graph Attention Network for — Wang 2020"
description: "Aspect-based sentiment analysis (ABSA) assigns a sentiment polarity to each aspect mentioned in a review."
authors: ["Kai Wang", "Weizhou Shen", "Yunyi Yang", "Xiaojun Quan", "Rui Wang"]
year: 2020
venue: "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics"
source: "https://aclanthology.org/2020.acl-main.295/"
doi: "10.18653/v1/2020.acl-main.295"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2020-relational-graph-attention-network-aspect/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wang-2020-relational-graph-attention-network-aspect/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2020](https://md.research.elamur.ai/free/en/papers/2020/) / Relational Graph Attention Network for Aspect-based Sentiment Analysis

# Relational Graph Attention Network for Aspect-based Sentiment Analysis

**Authors:** [Kai Wang](https://md.research.elamur.ai/free/en/authors/kai-wang/), [Weizhou Shen](https://md.research.elamur.ai/free/en/authors/weizhou-shen/), [Yunyi Yang](https://md.research.elamur.ai/free/en/authors/yunyi-yang/), [Xiaojun Quan](https://md.research.elamur.ai/free/en/authors/xiaojun-quan/), [Rui Wang](https://md.research.elamur.ai/free/en/authors/rui-wang/) · **Institutions:** [Alibaba Group](https://md.research.elamur.ai/free/en/companies/alibaba-group/) · [Sun Yat-sen University](https://md.research.elamur.ai/free/en/universities/sun-yat-sen-university/) · **Year:** 2020 · **Venue:** Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.acl-main.295/) · [PDF](https://aclanthology.org/2020.acl-main.295.pdf) · [DOI 10.18653/v1/2020.acl-main.295](https://doi.org/10.18653/v1/2020.acl-main.295) · [OpenAlex](https://openalex.org/W3035529900)

**Citations:** 665 citations (OpenAlex, 2026-10-14) · [110](https://md.research.elamur.ai/free/en/papers/wang-2020-relational-graph-attention-network-aspect/cited-by/) in our library, of the 140884 papers whose bibliographies we read

## Summary
Aspect-based sentiment analysis (ABSA) assigns a sentiment polarity to each aspect mentioned in a review. This is difficult when one sentence contains several aspects with opposing opinions, because attention-based models often link an aspect to the wrong opinion word. The authors argue that explicit syntax focused on the task can fix this.

They start from an ordinary dependency parse and rebuild it around the target aspect:
- The aspect becomes the root.
- Words directly attached to the aspect keep their original relation labels.
- All other words are attached to the root through a synthetic distance label, n:con.

Each aspect gets its own tree, and the uniform shape makes batching easy. The trees are encoded with R-GAT, a graph attention network extended with relation-aware attention heads over labeled edges. A variant built on BERT is also tested.

Experiments on the SemEval 2014 Restaurant and Laptop sets and a Twitter dataset show that R-GAT clearly beats plain GAT and the baselines, and it also improves BERT. The main takeaway is that dependency labels carry useful signal for linking aspects to opinions, beyond the shape of the tree.

## Key points
- **Data and code:** The model is evaluated on SemEval 2014 (Restaurant, Laptop) and Twitter. Code is released at github.com/shenwzh3/RGAT-ABSA.
- **Ablation:**
  - The aspect-oriented tree improves R-GAT on all three datasets but helps plain GAT only on Restaurant and Twitter.
  - Removing n:con lowers accuracy considerably, which the authors attribute to n:con compensating for parsing errors.
  - The best maximum value of n is 4.
  - In the extracted ablation table, full R-GAT appears to score about 83.30 (Restaurant), 77.42 (Laptop) and 75.57 (Twitter), but the table text is garbled.
- **Parsers:** Stanford Parser and Biaffine Parser were compared on UAS and LAS. The more accurate Biaffine parser led to higher sentiment accuracy, suggesting the method will gain from better parsing.
- **Error analysis:** The authors examined 100 misclassified Restaurant examples and grouped them into four causes.
  - For R-GAT: misleading neutral reviews 46%, comprehension 32%, advice 6%, double negation 6%.
  - For R-GAT+BERT: 50%, 31%, 5% and 3% respectively.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wang2020relational,
  title = {{Relational Graph Attention Network for Aspect-based Sentiment Analysis}},
  author = {Wang, Kai and Shen, Weizhou and Yang, Yunyi and Quan, Xiaojun and Wang, Rui},
  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.acl-main.295},
  url = {https://doi.org/10.18653/v1/2020.acl-main.295}
}
```

- [← previous Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://md.research.elamur.ai/free/en/papers/cui-2020-revisiting-pre-trained-models-chinese/)
- [next → Reformer: The Efficient Transformer](https://md.research.elamur.ai/free/en/papers/kitaev-2020-reformer-efficient-transformer/)
- [↑ back to papers of 2020](https://md.research.elamur.ai/free/en/papers/2020/)

## References

32 references, 18 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Danqi Chen and Christopher Manning. 2014. A fast and accurate dependency parser using neural networks. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 740–750. [find](https://scholar.google.com/scholar?q=Danqi+Chen+and+Christopher+Manning%2E+2014%2E+A+fast+and+accurate+dependency+parser+using+neural+networks%2E+In+Proceedings+of+the+2014+conference+on+empirical+methods+in+natural+language+processing+%28EMNLP%29) [our card](https://md.research.elamur.ai/free/en/papers/missing-2014-chen-fast-accurate-dependency-parser-neural/)
2. Peng Chen, Zhongqian Sun, Lidong Bing, and Wei Yang. 2017. Recurrent attention network on memory for aspect sentiment analysis. In Proceedings of the 2017 conference on empirical methods in natural language processing, pages 452–461. [find](https://scholar.google.com/scholar?q=Peng+Chen%2C+Zhongqian+Sun%2C+Lidong+Bing%2C+and+Wei+Yang%2E+2017%2E+Recurrent+attention+network+on+memory+for+aspect+sentiment+analysis%2E+In+Proceedings+of+the+2017+conference+on+empirical+methods+in+natural+la) [our card](https://md.research.elamur.ai/free/en/papers/chen-2017-recurrent-attention-network-memory-aspect/)
3. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. [find](https://scholar.google.com/scholar?q=Jacob+Devlin%2C+Ming%2DWei+Chang%2C+Kenton+Lee%2C+and+Kristina+Toutanova%2E+2018%2E+Bert%3A+Pre%2Dtraining+of+deep+bidirectional+transformers+for+language+understanding%2E+arXiv+preprint+arXiv%3A1810%2E04805%2E)
4. Li Dong, Furu Wei, Chuanqi Tan, Duyu Tang, Ming Zhou, and Ke Xu. 2014. Adaptive recursive neural network for target-dependent twitter sentiment classification. In Proceedings of the 52nd annual meeting of the association for computational linguistics (volume 2: Short papers), volume 2, pages 49–54. [find](https://scholar.google.com/scholar?q=Li+Dong%2C+Furu+Wei%2C+Chuanqi+Tan%2C+Duyu+Tang%2C+Ming+Zhou%2C+and+Ke+Xu%2E+2014%2E+Adaptive+recursive+neural+network+for+target%2Ddependent+twitter+sentiment+classification%2E+In+Proceedings+of+the+52nd+annual+meetin) [our card](https://md.research.elamur.ai/free/en/papers/dong-2014-adaptive-recursive-neural-network-target/)
5. Timothy Dozat and Christopher D Manning. 2016. Deep biaffine attention for neural dependency parsing. arXiv preprint arXiv:1611.01734. [find](https://scholar.google.com/scholar?q=Timothy+Dozat+and+Christopher+D+Manning%2E+2016%2E+Deep+biaffine+attention+for+neural+dependency+parsing%2E+arXiv+preprint+arXiv%3A1611%2E01734%2E) [our card](https://md.research.elamur.ai/free/en/papers/missing-2016-dozat-deep-biaffine-attention-neural-dependency/)
6. Feifan Fan, Yansong Feng, and Dongyan Zhao. 2018. Multi-grained attention network for aspect-level sentiment classification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3433–3442. [find](https://scholar.google.com/scholar?q=Feifan+Fan%2C+Yansong+Feng%2C+and+Dongyan+Zhao%2E+2018%2E+Multi%2Dgrained+attention+network+for+aspect%2Dlevel+sentiment+classification%2E+In+Proceedings+of+the+2018+Conference+on+Empirical+Methods+in+Natural+Langu)
7. Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson Liu, Matthew Peters, Michael Schmitz, and Luke Zettlemoyer. 2018. Allennlp: A deep semantic natural language processing platform. arXiv preprint arXiv:1803.07640. [find](https://scholar.google.com/scholar?q=Matt+Gardner%2C+Joel+Grus%2C+Mark+Neumann%2C+Oyvind+Tafjord%2C+Pradeep+Dasigi%2C+Nelson+Liu%2C+Matthew+Peters%2C+Michael+Schmitz%2C+and+Luke+Zettlemoyer%2E+2018%2E+Allennlp%3A+A+deep+semantic+natural+language+processing+pl) [our card](https://md.research.elamur.ai/free/en/papers/gardner-2018-allennlp-deep-semantic-natural-language/)
8. Ruidan He, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier. 2018a. Effective attention modeling for aspect-level sentiment classification. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1121–1131. [find](https://scholar.google.com/scholar?q=Ruidan+He%2C+Wee+Sun+Lee%2C+Hwee+Tou+Ng%2C+and+Daniel+Dahlmeier%2E+2018a%2E+Effective+attention+modeling+for+aspect%2Dlevel+sentiment+classification%2E+In+Proceedings+of+the+27th+International+Conference+on+Computa)
9. Shexia He, Zuchao Li, Hai Zhao, and Hongxiao Bai. 2018b. Syntax for semantic role labeling, to be, or not to be. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers, pages 2061–2071. Assoc… [find](https://scholar.google.com/scholar?q=Shexia+He%2C+Zuchao+Li%2C+Hai+Zhao%2C+and+Hongxiao+Bai%2E+2018b%2E+Syntax+for+semantic+role+labeling%2C+to+be%2C+or+not+to+be%2E+In+Proceedings+of+the+56th+Annual+Meeting+of+the+Association+for+Computational+Linguist)
10. Binxuan Huang and Kathleen Carley. 2019. Syntax-aware aspect level sentiment classification with graph attention networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP… [find](https://scholar.google.com/scholar?q=Binxuan+Huang+and+Kathleen+Carley%2E+2019%2E+Syntax%2Daware+aspect+level+sentiment+classification+with+graph+attention+networks%2E+In+Proceedings+of+the+2019+Conference+on+Empirical+Methods+in+Natural+Languag)
11. Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. [find](https://scholar.google.com/scholar?q=Diederik+P+Kingma+and+Jimmy+Ba%2E+2014%2E+Adam%3A+A+method+for+stochastic+optimization%2E+arXiv+preprint+arXiv%3A1412%2E6980%2E)
12. Himabindu Lakkaraju, Richard Socher, and Chris Manning. 2014. Aspect specific sentiment analysis using hierarchical deep learning. In NIPS Workshop on deep learning and representation learning. [find](https://scholar.google.com/scholar?q=Himabindu+Lakkaraju%2C+Richard+Socher%2C+and+Chris+Manning%2E+2014%2E+Aspect+specific+sentiment+analysis+using+hierarchical+deep+learning%2E+In+NIPS+Workshop+on+deep+learning+and+representation+learning%2E)
13. Cheng Li, Xiaoxiao Guo, and Qiaozhu Mei. 2017. Deep memory networks for attitude identification. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pages 671– 680. ACM. [find](https://scholar.google.com/scholar?q=Cheng+Li%2C+Xiaoxiao+Guo%2C+and+Qiaozhu+Mei%2E+2017%2E+Deep+memory+networks+for+attitude+identification%2E+In+Proceedings+of+the+Tenth+ACM+International+Conference+on+Web+Search+and+Data+Mining%2C+pages+671%E2%80%93+680%2E)
14. Xin Li, Lidong Bing, Wai Lam, and Bei Shi. 2018. Transformation networks for target-oriented sentiment classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 946– 956. [find](https://scholar.google.com/scholar?q=Xin+Li%2C+Lidong+Bing%2C+Wai+Lam%2C+and+Bei+Shi%2E+2018%2E+Transformation+networks+for+target%2Doriented+sentiment+classification%2E+In+Proceedings+of+the+56th+Annual+Meeting+of+the+Association+for+Computational+Li) [our card](https://md.research.elamur.ai/free/en/papers/li-2018-transformation-networks-target-oriented-sentiment/)
15. Kang Liu, Heng Li Xu, Yang Liu, and Jun Zhao. 2013. Opinion target extraction using partially-supervised word alignment model. In Twenty-Third International Joint Conference on Artificial Intelligence. [find](https://scholar.google.com/scholar?q=Kang+Liu%2C+Heng+Li+Xu%2C+Yang+Liu%2C+and+Jun+Zhao%2E+2013%2E+Opinion+target+extraction+using+partially%2Dsupervised+word+alignment+model%2E+In+Twenty%2DThird+International+Joint+Conference+on+Artificial+Intelligence)
16. Dehong Ma, Sujian Li, Xiaodong Zhang, and Houfeng Wang. 2017. Interactive attention networks for aspect-level sentiment classification. arXiv preprint arXiv:1709.00893. — [arXiv:1709.00893](https://arxiv.org/abs/1709.00893) [find](https://scholar.google.com/scholar?q=Dehong+Ma%2C+Sujian+Li%2C+Xiaodong+Zhang%2C+and+Houfeng+Wang%2E+2017%2E+Interactive+attention+networks+for+aspect%2Dlevel+sentiment+classification%2E+arXiv+preprint+arXiv%3A1709%2E00893%2E+%E2%80%94+%5BarXiv%3A1709%2E00893%5D%28https%3A%2F%2Far)
17. Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, and David McClosky. 2014. The stanford corenlp natural language processing toolkit. In Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations, pages 55–60. [find](https://scholar.google.com/scholar?q=Christopher+Manning%2C+Mihai+Surdeanu%2C+John+Bauer%2C+Jenny+Finkel%2C+Steven+Bethard%2C+and+David+McClosky%2E+2014%2E+The+stanford+corenlp+natural+language+processing+toolkit%2E+In+Proceedings+of+52nd+annual+meeting) [our card](https://md.research.elamur.ai/free/en/papers/manning-2014-stanford-corenlp-natural-language-processing/)
18. John Pavlopoulos Harris Papageorgiou Ion Androutsopoulos Maria Pontiki, Dimitris Galanis and Suresh Manandhar. 2014. Semeval-2014 task 4: Aspect based sentiment analysis. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 27–35. [find](https://scholar.google.com/scholar?q=John+Pavlopoulos+Harris+Papageorgiou+Ion+Androutsopoulos+Maria+Pontiki%2C+Dimitris+Galanis+and+Suresh+Manandhar%2E+2014%2E+Semeval%2D2014+task+4%3A+Aspect+based+sentiment+analysis%2E+In+Proceedings+of+the+8th+Int) [our card](https://md.research.elamur.ai/free/en/papers/pontiki-2014-semeval-2014-task-4-aspect/)
19. Thien Hai Nguyen and Kiyoaki Shirai. 2015. Phrasernn: Phrase recursive neural network for aspect-based sentiment analysis. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2509–2514. [find](https://scholar.google.com/scholar?q=Thien+Hai+Nguyen+and+Kiyoaki+Shirai%2E+2015%2E+Phrasernn%3A+Phrase+recursive+neural+network+for+aspect%2Dbased+sentiment+analysis%2E+In+Proceedings+of+the+2015+Conference+on+Empirical+Methods+in+Natural+Languag)
20. Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532–1543. [find](https://scholar.google.com/scholar?q=Jeffrey+Pennington%2C+Richard+Socher%2C+and+Christopher+Manning%2E+2014%2E+Glove%3A+Global+vectors+for+word+representation%2E+In+Proceedings+of+the+2014+conference+on+empirical+methods+in+natural+language+process)
21. Guang Qiu, Bing Liu, Jiajun Bu, and Chun Chen. 2011. Opinion word expansion and target extraction through double propagation. Computational linguistics, 37(1):9–27. [find](https://scholar.google.com/scholar?q=Guang+Qiu%2C+Bing+Liu%2C+Jiajun+Bu%2C+and+Chun+Chen%2E+2011%2E+Opinion+word+expansion+and+target+extraction+through+double+propagation%2E+Computational+linguistics%2C+37%281%29%3A9%E2%80%9327%2E) [our card](https://md.research.elamur.ai/free/en/papers/qiu-2011-opinion-word-expansion-target-extraction/)
22. Chi Sun, Luyao Huang, and Xipeng Qiu. 2019a. Utilizing bert for aspect-based sentiment analysis via constructing auxiliary sentence. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and S… [find](https://scholar.google.com/scholar?q=Chi+Sun%2C+Luyao+Huang%2C+and+Xipeng+Qiu%2E+2019a%2E+Utilizing+bert+for+aspect%2Dbased+sentiment+analysis+via+constructing+auxiliary+sentence%2E+In+Proceedings+of+the+2019+Conference+of+the+North+American+Chapter) [our card](https://md.research.elamur.ai/free/en/papers/sun-2019-utilizing-bert-aspect-based-sentiment/)
23. Kai Sun, Richong Zhang, Samuel Mensah, Yongyi Mao, and Xudong Liu. 2019b. Aspect-level sentiment analysis via convolution over dependency tree. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Pro… [find](https://scholar.google.com/scholar?q=Kai+Sun%2C+Richong+Zhang%2C+Samuel+Mensah%2C+Yongyi+Mao%2C+and+Xudong+Liu%2E+2019b%2E+Aspect%2Dlevel+sentiment+analysis+via+convolution+over+dependency+tree%2E+In+Proceedings+of+the+2019+Conference+on+Empirical+Metho) [our card](https://md.research.elamur.ai/free/en/papers/sun-2019-aspect-level-sentiment-analysis-convolution/)
24. Yuhao Zhang, Peng Qi, and Christopher D. Manning. 2018. Graph convolution over pruned dependency trees improves relation extraction. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, pages 2205–2215. Associati… [find](https://scholar.google.com/scholar?q=Yuhao+Zhang%2C+Peng+Qi%2C+and+Christopher+D%2E+Manning%2E+2018%2E+Graph+convolution+over+pruned+dependency+trees+improves+relation+extraction%2E+In+Proceedings+of+the+2018+Conference+on+Empirical+Methods+in+Natur) [our card](https://md.research.elamur.ai/free/en/papers/zhang-2018-graph-convolution-over-pruned-dependency/)
25. Duyu Tang, Bing Qin, and Ting Liu. 2016. Aspect level sentiment classification with deep memory network. arXiv preprint arXiv:1605.08900. [find](https://scholar.google.com/scholar?q=Duyu+Tang%2C+Bing+Qin%2C+and+Ting+Liu%2E+2016%2E+Aspect+level+sentiment+classification+with+deep+memory+network%2E+arXiv+preprint+arXiv%3A1605%2E08900%2E) [our card](https://md.research.elamur.ai/free/en/papers/tang-2016-aspect-level-sentiment-classification-deep/)
26. Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903. [find](https://scholar.google.com/scholar?q=Petar+Velic%CC%8Ckovic%CC%81%2C+Guillem+Cucurull%2C+Arantxa+Casanova%2C+Adriana+Romero%2C+Pietro+Lio%2C+and+Yoshua+Bengio%2E+2017%2E+Graph+attention+networks%2E+arXiv+preprint+arXiv%3A1710%2E10903%2E) [our card](https://md.research.elamur.ai/free/en/papers/veli-2017-vckovic-graph-attention-networks/)
27. Shuai Wang, Sahisnu Mazumder, Bing Liu, Mianwei Zhou, and Yi Chang. 2018. Target-sensitive memory networks for aspect sentiment classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 957–967. [find](https://scholar.google.com/scholar?q=Shuai+Wang%2C+Sahisnu+Mazumder%2C+Bing+Liu%2C+Mianwei+Zhou%2C+and+Yi+Chang%2E+2018%2E+Target%2Dsensitive+memory+networks+for+aspect+sentiment+classification%2E+In+Proceedings+of+the+56th+Annual+Meeting+of+the+Associa)
28. Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, and Xiaokui Xiao. 2016a. Recursive neural conditional random fields for aspect-based sentiment analysis. arXiv preprint arXiv:1603.06679. [find](https://scholar.google.com/scholar?q=Wenya+Wang%2C+Sinno+Jialin+Pan%2C+Daniel+Dahlmeier%2C+and+Xiaokui+Xiao%2E+2016a%2E+Recursive+neural+conditional+random+fields+for+aspect%2Dbased+sentiment+analysis%2E+arXiv+preprint+arXiv%3A1603%2E06679%2E) [our card](https://md.research.elamur.ai/free/en/papers/wang-2016-recursive-neural-conditional-random-fields/)
29. Yequan Wang, Minlie Huang, Li Zhao, et al. 2016b. Attention-based lstm for aspect-level sentiment classification. In Proceedings of the 2016 conference on empirical methods in natural language processing, pages 606–615. [find](https://scholar.google.com/scholar?q=Yequan+Wang%2C+Minlie+Huang%2C+Li+Zhao%2C+et+al%2E+2016b%2E+Attention%2Dbased+lstm+for+aspect%2Dlevel+sentiment+classification%2E+In+Proceedings+of+the+2016+conference+on+empirical+methods+in+natural+language+process) [our card](https://md.research.elamur.ai/free/en/papers/wang-2016-attention-based-lstm-aspect-level/)
30. Hu Xu, Bing Liu, Lei Shu, and S Yu Philip. 2019. Bert post-training for review reading comprehension and aspect-based sentiment analysis. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long… [find](https://scholar.google.com/scholar?q=Hu+Xu%2C+Bing+Liu%2C+Lei+Shu%2C+and+S+Yu+Philip%2E+2019%2E+Bert+post%2Dtraining+for+review+reading+comprehension+and+aspect%2Dbased+sentiment+analysis%2E+In+Proceedings+of+the+2019+Conference+of+the+North+American+Ch) [our card](https://md.research.elamur.ai/free/en/papers/xu-2019-bert-post-training-review-reading/)
31. Wei Xue and Tao Li. 2018. Aspect based sentiment analysis with gated convolutional networks. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2514–2523. [find](https://scholar.google.com/scholar?q=Wei+Xue+and+Tao+Li%2E+2018%2E+Aspect+based+sentiment+analysis+with+gated+convolutional+networks%2E+In+Proceedings+of+the+56th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Volume+1%3A+Long+)
32. Chen Zhang, Qiuchi Li, and Dawei Song. 2019. Aspect-based sentiment classification with aspect-specific graph convolutional networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (E… [find](https://scholar.google.com/scholar?q=Chen+Zhang%2C+Qiuchi+Li%2C+and+Dawei+Song%2E+2019%2E+Aspect%2Dbased+sentiment+classification+with+aspect%2Dspecific+graph+convolutional+networks%2E+In+Proceedings+of+the+2019+Conference+on+Empirical+Methods+in+Natu) [our card](https://md.research.elamur.ai/free/en/papers/zhang-2019-aspect-based-sentiment-classification-aspect/)