# BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis

arXiv: https://arxiv.org/abs/2204.03117 · HTML: https://mlsanity.com/paper/2204.03117/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 6 Apr 2022.

## 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)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [Relational Graph Attention Network for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2004.12362/) (2020)
- [Global Context Enhanced Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2106.05081/) (2021)
- [Fast and Accurate Neural CRF Constituency Parsing](https://mlsanity.com/paper/2008.03736/) (2020)
- [Attentional Encoder Network for Targeted Sentiment Classification](https://mlsanity.com/paper/1902.09314/) (2019)
- [Multi-view Intent Disentangle Graph Networks for Bundle Recommendation](https://mlsanity.com/paper/2202.11425/) (2022)
- [CAN: Constrained Attention Networks for Multi-Aspect Sentiment Analysis](https://mlsanity.com/paper/1812.10735/) (2018)
- [Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction](https://mlsanity.com/paper/2106.03044/) (2021)
- [Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification](https://mlsanity.com/paper/1811.10999/) (2018)
- [EmpDG: Multiresolution Interactive Empathetic Dialogue Generation](https://mlsanity.com/paper/1911.08698/) (2019)
- [Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment Classification](https://mlsanity.com/paper/1906.04501/) (2019)
- [Target Guided Emotion Aware Chat Machine](https://mlsanity.com/paper/2011.07432/) (2020)
- [Sentence Constituent-Aware Aspect-Category Sentiment Analysis with Graph Attention Networks](https://mlsanity.com/paper/2010.01461/) (2020)
- [PONE: A Novel Automatic Evaluation Metric for Open-Domain Generative Dialogue Systems](https://mlsanity.com/paper/2004.02399/) (2020)

## Built on by (most cited first, 11 of 11)

- [AoM: Detecting Aspect-oriented Information for Multimodal Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2306.01004/) (2023)
- [Temporal Knowledge Graph Question Answering: A Survey](https://mlsanity.com/paper/2406.14191/) (2024)
- [STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2211.15003/) (2022)
- [AspectCSE: Sentence Embeddings for Aspect-based Semantic Textual Similarity Using Contrastive Learning and Structured Knowledge](https://mlsanity.com/paper/2307.07851/) (2023)
- [Balanced Training Data Augmentation for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2507.09485/) (2025)
- [Large Language Models Enhanced by Plug and Play Syntactic Knowledge for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2506.12991/) (2025)
- [PGSO: Prompt-based Generative Sequence Optimization Network for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2412.00763/) (2024)
- [Extensible Multi-Granularity Fusion Network and Transferable Curriculum Learning for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2402.07787/) (2024)
- [Embarrassingly Simple Unsupervised Aspect Based Sentiment Tuple Extraction](https://mlsanity.com/paper/2404.13751/) (2024)
- [S2GSL: Incorporating Segment to Syntactic Enhanced Graph Structure Learning for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2406.02902/) (2024)
- [S2IT: Stepwise Syntax Integration Tuning for Large Language Models in Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2604.23296/) (2026)

## Abstract (arXiv)

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that aims to align aspects and corresponding sentiments for aspect-specific sentiment polarity inference. It is challenging because a sentence may contain multiple aspects or complicated (e.g., conditional, coordinating, or adversative) relations. Recently, exploiting dependency syntax information with graph neural networks has been the most popular trend. Despite its success, methods that heavily rely on the dependency tree pose challenges in accurately modeling the alignment of the aspects and their words indicative of sentiment, since the dependency tree may provide noisy signals of unrelated associations (e.g., the "conj" relation between "great" and "dreadful" in Figure 2). In this paper, to alleviate this problem, we propose a Bi-Syntax aware Graph Attention Network (BiSyn-GAT+). Specifically, BiSyn-GAT+ fully exploits the syntax information (e.g., phrase segmentation and hierarchical structure) of the constituent tree of a sentence to model the sentiment-aware context of every single aspect (called intra-context) and the sentiment relations across aspects (called inter-context) for learning. Experiments on four benchmark datasets demonstrate that BiSyn-GAT+ outperforms the state-of-the-art methods consistently.