# Structured Sentiment Analysis as Dependency Graph Parsing

arXiv: https://arxiv.org/abs/2105.14504 · HTML: https://mlsanity.com/paper/2105.14504/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 56 references that are in the map.
- First posted on arXiv 30 May 2021.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://mlsanity.com/paper/1503.00075/) (2015)
- [BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1904.02232/) (2019)
- [Named Entity Recognition as Dependency Parsing](https://mlsanity.com/paper/2005.07150/) (2020)
- [Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1911.01616/) (2019)
- [Exploiting BERT for End-to-End Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1910.00883/) (2019)
- [A Unified Model for Opinion Target Extraction and Target Sentiment Prediction](https://mlsanity.com/paper/1811.05082/) (2018)
- [An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/1906.06906/) (2019)
- [Simpler but More Accurate Semantic Dependency Parsing](https://mlsanity.com/paper/1807.01396/) (2018)
- [Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1603.06679/) (2016)
- [A Fine-Grained Sentiment Dataset for Norwegian](https://mlsanity.com/paper/1911.12722/) (2019)
- [MultiBooked: A Corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification](https://mlsanity.com/paper/1803.08614/) (2018)
- ['UFAL at MRP 2020: Permutation-invariant Semantic Parsing in PERIN](https://mlsanity.com/paper/2011.00758/) (2020)

## Built on by (most cited first, 28 of 28)

- [DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis](https://mlsanity.com/paper/2211.05705/) (2022)
- [Mastering the Explicit Opinion-role Interaction: Syntax-aided Neural Transition System for Unified Opinion Role Labeling](https://mlsanity.com/paper/2110.02001/) (2021)
- [Effective Token Graph Modeling using a Novel Labeling Strategy for Structured Sentiment Analysis](https://mlsanity.com/paper/2203.10796/) (2022)
- [Sparse Fuzzy Attention for Structured Sentiment Analysis](https://mlsanity.com/paper/2109.06719/) (2021)
- [Survey in Characterizing Semantic Change](https://mlsanity.com/paper/2402.19088/) (2024)
- [Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media](https://mlsanity.com/paper/2205.03296/) (2022)
- [EventGraph: Event Extraction as Semantic Graph Parsing](https://mlsanity.com/paper/2210.08646/) (2022)
- [Experiencer-Specific Emotion and Appraisal Prediction](https://mlsanity.com/paper/2210.12078/) (2022)
- [Dynamic Multi-Scale Context Aggregation for Conversational Aspect-Based Sentiment Quadruple Analysis](https://mlsanity.com/paper/2309.15476/) (2023)
- [Direct parsing to sentiment graphs](https://mlsanity.com/paper/2203.13209/) (2022)
- [A Syntax-Injected Approach for Faster and More Accurate Sentiment Analysis](https://mlsanity.com/paper/2406.15163/) (2024)
- [Dependency Graph Parsing as Sequence Labeling](https://mlsanity.com/paper/2410.17972/) (2024)
- [Towards Semantic Integration of Opinions: Unified Opinion Concepts Ontology and Extraction Task](https://mlsanity.com/paper/2505.18703/) (2025)
- [ETMS@IITKGP at SemEval-2022 Task 10: Structured Sentiment Analysis Using A Generative Approach](https://mlsanity.com/paper/2205.00440/) (2022)
- [Video Games as a Corpus: Sentiment Analysis using Fallout New Vegas Dialog](https://mlsanity.com/paper/2212.02168/) (2022)
- [Structured Sentiment Analysis as Transition-based Dependency Graph Parsing](https://mlsanity.com/paper/2305.05311/) (2023)
- [Adverbs, Surprisingly](https://mlsanity.com/paper/2305.19650/) (2023)
- [A Regularization-based Transfer Learning Method for Information Extraction via Instructed Graph Decoder](https://mlsanity.com/paper/2403.00891/) (2024)
- [Revisiting Structured Sentiment Analysis as Latent Dependency Graph Parsing](https://mlsanity.com/paper/2407.04801/) (2024)
- [Towards Temporal Knowledge-Base Creation for Fine-Grained Opinion Analysis with Language Models](https://mlsanity.com/paper/2509.02363/) (2025)
- [Opinion Extraction as A Structured Sentiment Analysis using Transformers](https://mlsanity.com/paper/2112.05056/) (2021)
- [LySACoru~na at SemEval-2022 Task 10: Repurposing Off-the-Shelf Tools for Sentiment Analysis as Semantic Dependency Parsing](https://mlsanity.com/paper/2204.12820/) (2022)
- [A Knowledge-Enhanced Adversarial Model for Cross-lingual Structured Sentiment Analysis](https://mlsanity.com/paper/2205.15514/) (2022)
- [Triple GNNs: Introducing Syntactic and Semantic Information for Conversational Aspect-Based Quadruple Sentiment Analysis](https://mlsanity.com/paper/2403.10065/) (2024)
- [Test-Time Code-Switching for Cross-lingual Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2501.14144/) (2025)
- [RuOpinionNE-2024: Extraction of Opinion Tuples from Russian News Texts](https://mlsanity.com/paper/2504.06947/) (2025)
- [Dialogues Aspect-based Sentiment Quadruple Extraction via Structural Entropy Minimization Partitioning](https://mlsanity.com/paper/2508.05023/) (2025)
- [ViTOED: A Dataset for Target-Oriented Emotion Detection on Vietnamese Social Media Texts](https://mlsanity.com/paper/2608.12776/) (2026)

## Abstract (arXiv)

Structured sentiment analysis attempts to extract full opinion tuples from a text, but over time this task has been subdivided into smaller and smaller sub-tasks, e,g,, target extraction or targeted polarity classification. We argue that this division has become counterproductive and propose a new unified framework to remedy the situation. We cast the structured sentiment problem as dependency graph parsing, where the nodes are spans of sentiment holders, targets and expressions, and the arcs are the relations between them. We perform experiments on five datasets in four languages (English, Norwegian, Basque, and Catalan) and show that this approach leads to strong improvements over state-of-the-art baselines. Our analysis shows that refining the sentiment graphs with syntactic dependency information further improves results.