# Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation

arXiv: https://arxiv.org/abs/2010.13389 · HTML: https://mlsanity.com/paper/2010.13389/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [Multimodal learning with graphs](https://mlsanity.com/paper/2209.03299/) (2022)
- [On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training](https://mlsanity.com/paper/2304.09563/) (2023)
- [Word Graph Guided Summarization for Radiology Findings](https://mlsanity.com/paper/2112.09925/) (2021)
- [Balanced Training Data Augmentation for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2507.09485/) (2025)
- [Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfer](https://mlsanity.com/paper/2109.03819/) (2021)
- [Semantic Novelty Detection and Characterization in Factual Text Involving Named Entities](https://mlsanity.com/paper/2210.17440/) (2022)
- [Enhancing Language Models for Robust Greenwashing Detection](https://mlsanity.com/paper/2601.21722/) (2026)

## Abstract (arXiv)

Aspect-based Sentiment Analysis (ABSA) seeks to predict the sentiment polarity of a sentence toward a specific aspect. Recently, it has been shown that dependency trees can be integrated into deep learning models to produce the state-of-the-art performance for ABSA. However, these models tend to compute the hidden/representation vectors without considering the aspect terms and fail to benefit from the overall contextual importance scores of the words that can be obtained from the dependency tree for ABSA. In this work, we propose a novel graph-based deep learning model to overcome these two issues of the prior work on ABSA. In our model, gate vectors are generated from the representation vectors of the aspect terms to customize the hidden vectors of the graph-based models toward the aspect terms. In addition, we propose a mechanism to obtain the importance scores for each word in the sentences based on the dependency trees that are then injected into the model to improve the representation vectors for ABSA. The proposed model achieves the state-of-the-art performance on three benchmark datasets.