---
title: "Aspect-Category-Opinion-Sentiment Quadruple — Cai 2021"
description: "Aspect-based sentiment analysis (ABSA) systems have mostly assumed that the opinion target and the opinion word both appear explicitly in a review."
authors: ["Hongjie Cai", "Rui Xia", "Jianfei Yu"]
year: 2021
venue: "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)"
source: "https://aclanthology.org/2021.acl-long.29/"
doi: "10.18653/v1/2021.acl-long.29"
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/cai-2021-aspect-category-opinion-sentiment-quadruple/"
---
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# Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions

**Authors:** Hongjie Cai, Rui Xia, Jianfei Yu · **Year:** 2021 · **Venue:** Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.acl-long.29/) · [PDF](https://aclanthology.org/2021.acl-long.29.pdf) · [DOI 10.18653/v1/2021.acl-long.29](https://doi.org/10.18653/v1/2021.acl-long.29) · [OpenAlex](https://openalex.org/W3174994995)

**Citations:** 163 citations (openalex-snapshot · 2026-10-14) · [92](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Aspect-based sentiment analysis (ABSA) systems have mostly assumed that the opinion target and the opinion word both appear explicitly in a review. Real product reviews often leave one or both unstated. The authors address this gap with a new task, Aspect-Category-Opinion-Sentiment (ACOS) Quadruple Extraction. For each sentence, the system must produce every aspect–category–opinion–sentiment combination. A missing aspect or opinion is marked as NULL, so the category or sentiment then carries the meaning.

To support the task, they built two annotated corpora:
- **Restaurant-ACOS** extends the SemEval 2016 Restaurant data and its later opinion annotations with quadruples and implicit opinions.
- **Laptop-ACOS** is a new, larger collection of Amazon laptop reviews, annotated from scratch.

They adapted four existing pair and triple extraction methods into ACOS baselines. In their analysis, the pipeline model Extract-Classify-ACOS handled implicit cases most reliably. The work matters because it offers a single framework and shared benchmark for implicit aspects and opinions, which earlier studies treated separately or ignored.

## Key points
- **Restaurant-ACOS:** 2,286 sentences, 3,658 quadruples and 13 categories. **Laptop-ACOS:** 4,076 sentences, 5,758 quadruples and 121 categories, using the SemEval 2016 Laptop category scheme. The laptop reviews were collected on Amazon in 2017–2018.
- Implicit cases are common. In the laptop domain, close to 44% of sentences include an implicit aspect or opinion, and over 8% include both.
- Two PhD-student annotators labelled the data independently and reached 75.86% strict quadruple-match F1. A third expert settled disagreements.
- **Baselines:** Double-Propagation-ACOS, JET-ACOS, TAS-BERT-ACOS and Extract-Classify-ACOS. Only the last two can produce quadruples with implicit aspects and opinions, and Extract-Classify-ACOS gave the best results overall.
- Both datasets and the code for the four baselines are publicly available at github.com/NUSTM/ACOS.

*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/)

## Cited by

Cited by

92 papers of our library cite it (of the 140884 whose bibliographies we read); 2 of them have a page here, 90 not yet.

- [Extending Czech Aspect-Based Sentiment Analysis with Opinion Terms: Dataset and LLM Benchmarks](https://md.research.elamur.ai/free/en/papers/smid-2026-extending-czech-aspect-based-sentiment/)
- [Sentiment Analysis in the Era of Large Language Models: A Reality Check](https://md.research.elamur.ai/free/en/papers/zhang-2023-sentiment-analysis-era-large-language/)

## Cite

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

```bibtex
@inproceedings{cai2021aspect,
  title = {{Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions}},
  author = {Cai, Hongjie and Xia, Rui and Yu, Jianfei},
  booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  year = {2021},
  doi = {10.18653/v1/2021.acl-long.29},
  url = {https://doi.org/10.18653/v1/2021.acl-long.29}
}
```

- [← previous Aspect Sentiment Quad Prediction as Paraphrase Generation](https://md.research.elamur.ai/free/en/papers/zhang-2021-aspect-sentiment-quad-prediction-as/)
- [next → AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models](https://md.research.elamur.ai/free/en/papers/madabushi-2021-astitchinlanguagemodels-dataset-methods-exploration-idiomaticity/)
- [↑ back to papers of 2021](https://md.research.elamur.ai/free/en/papers/2021/)

## References

47 references, 11 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Huan-Yuan Chen and Hsin-Hsi Chen. 2016. Implicit polarity and implicit aspect recognition in opinion mining. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), pages 20–25. [find](https://scholar.google.com/scholar?q=Huan%2DYuan+Chen+and+Hsin%2DHsi+Chen%2E+2016%2E+Implicit+polarity+and+implicit+aspect+recognition+in+opinion+mining%2E+In+Proceedings+of+the+54th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+)
2. Shaowei Chen, Jie Liu, Yu Wang, Wenzheng Zhang, and Ziming Chi. 2020a. Synchronous double-channel recurrent network for aspect-opinion pair extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pages 6515–6524. [find](https://scholar.google.com/scholar?q=Shaowei+Chen%2C+Jie+Liu%2C+Yu+Wang%2C+Wenzheng+Zhang%2C+and+Ziming+Chi%2E+2020a%2E+Synchronous+double%2Dchannel+recurrent+network+for+aspect%2Dopinion+pair+extraction%2E+In+Proceedings+of+the+58th+Annual+Meeting+of+the)
3. Shaowei Chen, Yu Wang, Jie Liu, and Yuelin Wang. 2021. Bidirectional machine reading comprehension for aspect sentiment triplet extraction. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), pages 12666–12674. [find](https://scholar.google.com/scholar?q=Shaowei+Chen%2C+Yu+Wang%2C+Jie+Liu%2C+and+Yuelin+Wang%2E+2021%2E+Bidirectional+machine+reading+comprehension+for+aspect+sentiment+triplet+extraction%2E+In+Proceedings+of+the+35th+AAAI+Conference+on+Artificial+Int)
4. Xiao Chen, Changlong Sun, Jingjing Wang, Shoushan Li, Luo Si, Min Zhang, and Guodong Zhou. 2020b. Aspect sentiment classification with document-level sentiment preference modeling. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pages 3667–3677. [find](https://scholar.google.com/scholar?q=Xiao+Chen%2C+Changlong+Sun%2C+Jingjing+Wang%2C+Shoushan+Li%2C+Luo+Si%2C+Min+Zhang%2C+and+Guodong+Zhou%2E+2020b%2E+Aspect+sentiment+classification+with+document%2Dlevel+sentiment+preference+modeling%2E+In+Proceedings+of+t)
5. Zhuang Chen and Tieyun Qian. 2020. Relation-aware collaborative learning for unified aspect-based sentiment analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pages 3685–3694. [find](https://scholar.google.com/scholar?q=Zhuang+Chen+and+Tieyun+Qian%2E+2020%2E+Relation%2Daware+collaborative+learning+for+unified+aspect%2Dbased+sentiment+analysis%2E+In+Proceedings+of+the+58th+Annual+Meeting+of+the+Association+for+Computational+Lin)
6. Xin Li, Lidong Bing, Piji Li, Wai Lam, and Zhimou Yang. 2018. Aspect term extraction with history attention and selective transformation. arXiv preprint arXiv:1805.00760. — [arXiv:1805.00760](https://arxiv.org/abs/1805.00760) [find](https://scholar.google.com/scholar?q=Xin+Li%2C+Lidong+Bing%2C+Piji+Li%2C+Wai+Lam%2C+and+Zhimou+Yang%2E+2018%2E+Aspect+term+extraction+with+history+attention+and+selective+transformation%2E+arXiv+preprint+arXiv%3A1805%2E00760%2E+%E2%80%94+%5BarXiv%3A1805%2E00760%5D%28https%3A%2F%2F)
7. Bing Liu. 2012. Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, pages 1–167. [find](https://scholar.google.com/scholar?q=Bing+Liu%2E+2012%2E+Sentiment+analysis+and+opinion+mining%2E+Synthesis+lectures+on+human+language+technologies%2C+pages+1%E2%80%93167%2E)
8. Hongliang Dai and Yangqiu Song. 2019. Neural aspect and opinion term extraction with mined rules as weak supervision. arXiv preprint arXiv:1907.03750. [find](https://scholar.google.com/scholar?q=Hongliang+Dai+and+Yangqiu+Song%2E+2019%2E+Neural+aspect+and+opinion+term+extraction+with+mined+rules+as+weak+supervision%2E+arXiv+preprint+arXiv%3A1907%2E03750%2E)
9. 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)
10. Zhifang Fan, Zhen Wu, Xinyu Dai, Shujian Huang, and Jiajun Chen. 2019. Target-oriented opinion words extraction with target-fused neural sequence labeling. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), pages 2509–2518. [find](https://scholar.google.com/scholar?q=Zhifang+Fan%2C+Zhen+Wu%2C+Xinyu+Dai%2C+Shujian+Huang%2C+and+Jiajun+Chen%2E+2019%2E+Target%2Doriented+opinion+words+extraction+with+target%2Dfused+neural+sequence+labeling%2E+In+Proceedings+of+the+2019+Conference+of+the)
11. Ruidan He, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier. 2019. An interactive multi-task learning network for end-to-end aspect-based sentiment analysis. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), pages 504–515. [find](https://scholar.google.com/scholar?q=Ruidan+He%2C+Wee+Sun+Lee%2C+Hwee+Tou+Ng%2C+and+Daniel+Dahlmeier%2E+2019%2E+An+interactive+multi%2Dtask+learning+network+for+end%2Dto%2Dend+aspect%2Dbased+sentiment+analysis%2E+In+Proceedings+of+the+57th+Annual+Meeting+of) [our card](https://md.research.elamur.ai/free/en/papers/he-2019-interactive-multi-task-learning-network/)
12. Minghao Hu, Yuxing Peng, Zhen Huang, Dongsheng Li, and Yiwei Lv. 2019. Open-domain targeted sentiment analysis via span-based extraction and classification. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), pages 537–546. [find](https://scholar.google.com/scholar?q=Minghao+Hu%2C+Yuxing+Peng%2C+Zhen+Huang%2C+Dongsheng+Li%2C+and+Yiwei+Lv%2E+2019%2E+Open%2Ddomain+targeted+sentiment+analysis+via+span%2Dbased+extraction+and+classification%2E+In+Proceedings+of+the+57th+Annual+Meeting+o)
13. Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 168–177. [find](https://scholar.google.com/scholar?q=Minqing+Hu+and+Bing+Liu%2E+2004%2E+Mining+and+summarizing+customer+reviews%2E+In+Proceedings+of+the+tenth+ACM+SIGKDD+international+conference+on+Knowledge+discovery+and+data+mining%2C+pages+168%E2%80%93177%2E)
14. Long Jiang, Mo Yu, Ming Zhou, Xiaohua Liu, and Tiejun Zhao. 2011. Target-dependent twitter sentiment classification. In Proceedings of the 49th annual Meeting of the association for computational linguistics (ACL), pages 151–160. [find](https://scholar.google.com/scholar?q=Long+Jiang%2C+Mo+Yu%2C+Ming+Zhou%2C+Xiaohua+Liu%2C+and+Tiejun+Zhao%2E+2011%2E+Target%2Ddependent+twitter+sentiment+classification%2E+In+Proceedings+of+the+49th+annual+Meeting+of+the+association+for+computational+ling)
15. Evgeny Kim and Roman Klinger. 2018. Who feels what and why? annotation of a literature corpus with semantic roles of emotions. In Proceedings of the 27th International Conference on Computational Linguistics (COLING), pages 1345–1359. [find](https://scholar.google.com/scholar?q=Evgeny+Kim+and+Roman+Klinger%2E+2018%2E+Who+feels+what+and+why%3F+annotation+of+a+literature+corpus+with+semantic+roles+of+emotions%2E+In+Proceedings+of+the+27th+International+Conference+on+Computational+Ling)
16. Farek Lazhar and Yamina Tlili Guiyassa. 2016. Mining explicit and implicit opinions from reviews. Int. J. Data Min. Model. Manag., 8:75–92. [find](https://scholar.google.com/scholar?q=Farek+Lazhar+and+Yamina+Tlili+Guiyassa%2E+2016%2E+Mining+explicit+and+implicit+opinions+from+reviews%2E+Int%2E+J%2E+Data+Min%2E+Model%2E+Manag%2E%2C+8%3A75%E2%80%9392%2E)
17. Xin Li, Lidong Bing, Piji Li, and Wai Lam. 2019. A unified model for opinion target extraction and target sentiment prediction. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI), pages 6714–6721. [find](https://scholar.google.com/scholar?q=Xin+Li%2C+Lidong+Bing%2C+Piji+Li%2C+and+Wai+Lam%2E+2019%2E+A+unified+model+for+opinion+target+extraction+and+target+sentiment+prediction%2E+In+Proceedings+of+the+33rd+AAAI+Conference+on+Artificial+Intelligence+%28A)
18. Bing Liu, Minqing Hu, and Junsheng Cheng. 2005. Opinion observer: analyzing and comparing opinions on the web. In Proceedings of the 14th international conference on World Wide Web (WWW), pages 342–351. [find](https://scholar.google.com/scholar?q=Bing+Liu%2C+Minqing+Hu%2C+and+Junsheng+Cheng%2E+2005%2E+Opinion+observer%3A+analyzing+and+comparing+opinions+on+the+web%2E+In+Proceedings+of+the+14th+international+conference+on+World+Wide+Web+%28WWW%29%2C+pages+342%E2%80%9335)
19. Huaishao Luo, Tianrui Li, Bing Liu, and Junbo Zhang. 2019. Doer: Dual cross-shared rnn for aspect term-polarity co-extraction. arXiv preprint arXiv:1906.01794. [find](https://scholar.google.com/scholar?q=Huaishao+Luo%2C+Tianrui+Li%2C+Bing+Liu%2C+and+Junbo+Zhang%2E+2019%2E+Doer%3A+Dual+cross%2Dshared+rnn+for+aspect+term%2Dpolarity+co%2Dextraction%2E+arXiv+preprint+arXiv%3A1906%2E01794%2E)
20. 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)
21. Yue Mao, Yi Shen, Chao Yu, and Longjun Cai. 2021. A joint training dual-mrc framework for aspect based sentiment analysis. arXiv preprint arXiv:2101.00816. — [arXiv:2101.00816](https://arxiv.org/abs/2101.00816) [find](https://scholar.google.com/scholar?q=Yue+Mao%2C+Yi+Shen%2C+Chao+Yu%2C+and+Longjun+Cai%2E+2021%2E+A+joint+training+dual%2Dmrc+framework+for+aspect+based+sentiment+analysis%2E+arXiv+preprint+arXiv%3A2101%2E00816%2E+%E2%80%94+%5BarXiv%3A2101%2E00816%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2)
22. Margaret Mitchell, Jacqui Aguilar, Theresa Wilson, and Benjamin Van Durme. 2013. Open domain targeted sentiment. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1643–1654. [find](https://scholar.google.com/scholar?q=Margaret+Mitchell%2C+Jacqui+Aguilar%2C+Theresa+Wilson%2C+and+Benjamin+Van+Durme%2E+2013%2E+Open+domain+targeted+sentiment%2E+In+Proceedings+of+the+2013+Conference+on+Empirical+Methods+in+Natural+Language+Processi) [our card](https://md.research.elamur.ai/free/en/papers/mitchell-2013-open-domain-targeted-sentiment/)
23. Haiyun Peng, Lu Xu, Lidong Bing, Fei Huang, Wei Lu, and Luo Si. 2020. Knowing what, how and why: A near complete solution for aspect-based sentiment analysis. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI), pages 8600– 8607. [find](https://scholar.google.com/scholar?q=Haiyun+Peng%2C+Lu+Xu%2C+Lidong+Bing%2C+Fei+Huang%2C+Wei+Lu%2C+and+Luo+Si%2E+2020%2E+Knowing+what%2C+how+and+why%3A+A+near+complete+solution+for+aspect%2Dbased+sentiment+analysis%2E+In+Proceedings+of+the+34th+AAAI+Conferenc)
24. Maria Pontiki, Dimitrios Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, Mohammad Al-Smadi, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orphée De Clercq, et al. 2016. Semeval-2016 task 5: Aspect based sentiment analysis. In International workshop on semantic evaluation, pages 19–3… [find](https://scholar.google.com/scholar?q=Maria+Pontiki%2C+Dimitrios+Galanis%2C+Haris+Papageorgiou%2C+Ion+Androutsopoulos%2C+Suresh+Manandhar%2C+Mohammad+Al%2DSmadi%2C+Mahmoud+Al%2DAyyoub%2C+Yanyan+Zhao%2C+Bing+Qin%2C+Orphe%CC%81e+De+Clercq%2C+et+al%2E+2016%2E+Semeval%2D2016+t) [our card](https://md.research.elamur.ai/free/en/papers/pontiki-2016-semeval-2016-task-5-aspect/)
25. Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, 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, Dublin, Ireland. Associa… [find](https://scholar.google.com/scholar?q=Maria+Pontiki%2C+Dimitris+Galanis%2C+John+Pavlopoulos%2C+Harris+Papageorgiou%2C+Ion+Androutsopoulos%2C+and+Suresh+Manandhar%2E+2014%2E+SemEval%2D2014+task+4%3A+Aspect+based+sentiment+analysis%2E+In+Proceedings+of+the+8th) [our card](https://md.research.elamur.ai/free/en/papers/pontiki-2014-semeval-2014-task-4-aspect/)
26. Soujanya Poria, Erik Cambria, Lun-Wei Ku, Chen Gui, and Alexander Gelbukh. 2014. A rule-based approach to aspect extraction from product reviews. In Proceedings of the second workshop on natural language processing for social media (SocialNLP), pages 28–37. [find](https://scholar.google.com/scholar?q=Soujanya+Poria%2C+Erik+Cambria%2C+Lun%2DWei+Ku%2C+Chen+Gui%2C+and+Alexander+Gelbukh%2E+2014%2E+A+rule%2Dbased+approach+to+aspect+extraction+from+product+reviews%2E+In+Proceedings+of+the+second+workshop+on+natural+langu)
27. 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/)
28. Duyu Tang, Bing Qin, Xiaocheng Feng, and Ting Liu. 2015. Effective lstms for target-dependent sentiment classification. arXiv preprint arXiv:1512.01100. [find](https://scholar.google.com/scholar?q=Duyu+Tang%2C+Bing+Qin%2C+Xiaocheng+Feng%2C+and+Ting+Liu%2E+2015%2E+Effective+lstms+for+target%2Ddependent+sentiment+classification%2E+arXiv+preprint+arXiv%3A1512%2E01100%2E) [our card](https://md.research.elamur.ai/free/en/papers/tang-2016-effective-lstms-target-dependent-sentiment/)
29. 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/)
30. Zhen Wu, Chengcan Ying, Fei Zhao, Zhifang Fan, Xinyu Dai, and Rui Xia. 2020. Grid tagging scheme for end-to-end fine-grained opinion extraction. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pages 2576–2585. [find](https://scholar.google.com/scholar?q=Zhen+Wu%2C+Chengcan+Ying%2C+Fei+Zhao%2C+Zhifang+Fan%2C+Xinyu+Dai%2C+and+Rui+Xia%2E+2020%2E+Grid+tagging+scheme+for+end%2Dto%2Dend+fine%2Dgrained+opinion+extraction%2E+In+Proceedings+of+the+2020+Conference+on+Empirical+Meth)
31. 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 (NAACL), pages 2324–2335. [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/)
32. Hao Tang, Donghong Ji, Chenliang Li, and Qiji Zhou. 2020. Dependency graph enhanced dual-transformer structure for aspect-based sentiment classification. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pages 6578–6588. [find](https://scholar.google.com/scholar?q=Hao+Tang%2C+Donghong+Ji%2C+Chenliang+Li%2C+and+Qiji+Zhou%2E+2020%2E+Dependency+graph+enhanced+dual%2Dtransformer+structure+for+aspect%2Dbased+sentiment+classification%2E+In+Proceedings+of+the+58th+Annual+Meeting+of+t)
33. Lu Xu, Hao Li, Wei Lu, and Lidong Bing. 2020. Position-aware tagging for aspect sentiment triplet extraction. arXiv preprint arXiv:2010.02609. [find](https://scholar.google.com/scholar?q=Lu+Xu%2C+Hao+Li%2C+Wei+Lu%2C+and+Lidong+Bing%2E+2020%2E+Position%2Daware+tagging+for+aspect+sentiment+triplet+extraction%2E+arXiv+preprint+arXiv%3A2010%2E02609%2E) [our card](https://md.research.elamur.ai/free/en/papers/xu-2020-position-aware-tagging-aspect-sentiment/)
34. Duy-Tin Vo and Yue Zhang. 2015. Target-dependent twitter sentiment classification with rich automatic features. In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI), pages 1347–1353. [find](https://scholar.google.com/scholar?q=Duy%2DTin+Vo+and+Yue+Zhang%2E+2015%2E+Target%2Ddependent+twitter+sentiment+classification+with+rich+automatic+features%2E+In+Proceedings+of+the+24th+International+Joint+Conference+on+Artificial+Intelligence+%28IJ)
35. Min Yang, Wenting Tu, Jingxuan Wang, Fei Xu, and Xiaojun Chen. 2017b. Attention based lstm for target dependent sentiment classification. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI), pages 5013–5014. [find](https://scholar.google.com/scholar?q=Min+Yang%2C+Wenting+Tu%2C+Jingxuan+Wang%2C+Fei+Xu%2C+and+Xiaojun+Chen%2E+2017b%2E+Attention+based+lstm+for+target+dependent+sentiment+classification%2E+In+Proceedings+of+the+Thirty%2DFirst+AAAI+Conference+on+Artifici)
36. Hai Wan, Yufei Yang, Jianfeng Du, Yanan Liu, Kunxun Qi, and Jeff Z Pan. 2020. Target-aspect-sentiment joint detection for aspect-based sentiment analysis. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI), pages 9122–9129. [find](https://scholar.google.com/scholar?q=Hai+Wan%2C+Yufei+Yang%2C+Jianfeng+Du%2C+Yanan+Liu%2C+Kunxun+Qi%2C+and+Jeff+Z+Pan%2E+2020%2E+Target%2Daspect%2Dsentiment+joint+detection+for+aspect%2Dbased+sentiment+analysis%2E+In+Proceedings+of+the+34th+AAAI+Conference+on)
37. Jianfei Yu, Jing Jiang, and Rui Xia. 2018. Global inference for aspect and opinion terms co-extraction based on multi-task neural networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27(1):168–177. [find](https://scholar.google.com/scholar?q=Jianfei+Yu%2C+Jing+Jiang%2C+and+Rui+Xia%2E+2018%2E+Global+inference+for+aspect+and+opinion+terms+co%2Dextraction+based+on+multi%2Dtask+neural+networks%2E+IEEE%2FACM+Transactions+on+Audio%2C+Speech%2C+and+Language+Process)
38. Jingjing Wang, Changlong Sun, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, and Guodong Zhou. 2019. Aspect sentiment classification towards question-answering with reinforced bidirectional attention network. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics… [find](https://scholar.google.com/scholar?q=Jingjing+Wang%2C+Changlong+Sun%2C+Shoushan+Li%2C+Xiaozhong+Liu%2C+Luo+Si%2C+Min+Zhang%2C+and+Guodong+Zhou%2E+2019%2E+Aspect+sentiment+classification+towards+question%2Danswering+with+reinforced+bidirectional+attention+)
39. Lei Zhang, Shuai Wang, and Bing Liu. 2018. Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4):e1253. [find](https://scholar.google.com/scholar?q=Lei+Zhang%2C+Shuai+Wang%2C+and+Bing+Liu%2E+2018%2E+Deep+learning+for+sentiment+analysis%3A+A+survey%2E+Wiley+Interdisciplinary+Reviews%3A+Data+Mining+and+Knowledge+Discovery%2C+8%284%29%3Ae1253%2E)
40. 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 (ACL), 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)
41. 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/)
42. Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, and Xiaokui Xiao. 2017. Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), pages 3316–3322. [find](https://scholar.google.com/scholar?q=Wenya+Wang%2C+Sinno+Jialin+Pan%2C+Daniel+Dahlmeier%2C+and+Xiaokui+Xiao%2E+2017%2E+Coupled+multi%2Dlayer+attentions+for+co%2Dextraction+of+aspect+and+opinion+terms%2E+In+Proceedings+of+the+31st+AAAI+Conference+on+Arti)
43. Yequan Wang, Minlie Huang, Xiaoyan Zhu, and Li Zhao. 2016b. Attention-based lstm for aspect-level sentiment classification. In Proceedings of the 2016 conference on empirical methods in natural language processing (EMNLP), pages 606–615. [find](https://scholar.google.com/scholar?q=Yequan+Wang%2C+Minlie+Huang%2C+Xiaoyan+Zhu%2C+and+Li+Zhao%2E+2016b%2E+Attention%2Dbased+lstm+for+aspect%2Dlevel+sentiment+classification%2E+In+Proceedings+of+the+2016+conference+on+empirical+methods+in+natural+langua) [our card](https://md.research.elamur.ai/free/en/papers/wang-2016-attention-based-lstm-aspect-level/)
44. Jie Yang, Yue Zhang, Linwei Li, and Xingxuan Li. 2017a. Yedda: A lightweight collaborative text span annotation tool. arXiv preprint arXiv:1711.03759. [find](https://scholar.google.com/scholar?q=Jie+Yang%2C+Yue+Zhang%2C+Linwei+Li%2C+and+Xingxuan+Li%2E+2017a%2E+Yedda%3A+A+lightweight+collaborative+text+span+annotation+tool%2E+arXiv+preprint+arXiv%3A1711%2E03759%2E)
45. Meishan Zhang, Yue Zhang, and Duy-Tin Vo. 2015. Neural networks for open domain targeted sentiment. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 612–621. [find](https://scholar.google.com/scholar?q=Meishan+Zhang%2C+Yue+Zhang%2C+and+Duy%2DTin+Vo%2E+2015%2E+Neural+networks+for+open+domain+targeted+sentiment%2E+In+Proceedings+of+the+2015+Conference+on+Empirical+Methods+in+Natural+Language+Processing+%28EMNLP%29%2C+p)
46. Meishan Zhang, Yue Zhang, and Duy-Tin Vo. 2016. Gated neural networks for targeted sentiment analysis. In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI), pages 3087–3093. [find](https://scholar.google.com/scholar?q=Meishan+Zhang%2C+Yue+Zhang%2C+and+Duy%2DTin+Vo%2E+2016%2E+Gated+neural+networks+for+targeted+sentiment+analysis%2E+In+Proceedings+of+the+30th+AAAI+Conference+on+Artificial+Intelligence+%28AAAI%29%2C+pages+3087%E2%80%933093%2E)
47. He Zhao, Longtao Huang, Rong Zhang, Quan Lu, et al. 2020. Spanmlt: A span-based multi-task learning framework for pair-wise aspect and opinion terms extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pages 3239–3248. [find](https://scholar.google.com/scholar?q=He+Zhao%2C+Longtao+Huang%2C+Rong+Zhang%2C+Quan+Lu%2C+et+al%2E+2020%2E+Spanmlt%3A+A+span%2Dbased+multi%2Dtask+learning+framework+for+pair%2Dwise+aspect+and+opinion+terms+extraction%2E+In+Proceedings+of+the+58th+Annual+Meeti)