Aspect-Based Sentiment Analysis Through Graph Convolutional Networks and Joint Task Learning
Abstract
:1. Introduction
- This study presents an innovative aspect-based sentiment analysis model (MTL-GCN) that integrates graph convolutional networks into a joint task learning framework. By introducing a feature-sharing mechanism to promote information interaction between tasks, aspect term extraction is effectively leveraged to provide deep semantic features for the sentiment classification task, thereby improving the overall performance.
- In terms of method design, we redesign the traditional GCN. By introducing the relative positional information of nodes, P-GCNs are proposed, which significantly enhance the modeling capability of syntactic dependencies and focus on the positional information of aspect terms.
- We propose a context feature representation method that combines graph convolutional networks with the multi-head attention mechanism, comprehensively integrating the local and global semantic information within the text.
- Finally, experiments on multiple benchmark datasets validate the superior performance of the proposed model in aspect term extraction and sentiment classification tasks.
2. Related Work
3. Methodology
3.1. Task Definition
3.2. Overview of the Model Framework
3.3. Context-Encoder
3.4. Aspect Terms Extraction
3.4.1. Construction of the Dependency Tree
3.4.2. Calculation of the Weight Matrix
3.4.3. Position-Focused Graph Convolutional Networks
3.5. Contextual Feature Representation
Multi-Head Attention Mechanism and Graph Convolutional Network
3.6. Sentiment Polarity Classification
3.7. Model Training
4. Experiments
4.1. Datasets and Parameter Settings
4.1.1. Datasets
4.1.2. Experimental Parameter Settings
4.2. Evaluation and Criteria
4.3. Compared Models
4.3.1. Aspect Term Extraction Model
- DTBCSNN [24]: A dependency tree-based stacked convolutional neural network was proposed, which uses conditional random fields (CRFs) to accurately extract aspect terms.
- RAL [25]: A reinforcement learning-based active learning sampling strategy was proposed to optimize the aspect term extraction process, improving extraction efficiency and accuracy.
- LDA [26]: An unsupervised learning method was proposed, which identifies potential topics of user interest and achieves automatic aspect term extraction through the guidance of a small set of seed words.
- DA-DCGCN [10]: A method for aspect term extraction combining dynamic attention mechanism and dense connection graph convolutional network (DA-DCGCN) is proposed.
4.3.2. Aspect Sentiment Classification Model
- DualGCN [27]: A dual graph convolutional network model including SynGCN and SemGCN modules for capturing syntactic structures and semantic connections separately.
- SDGCN-BERT [28]: An ABSA model through graph convolutional networks. By introducing a bi-directional attention mechanism with positional encoding and a GCN module, the model effectively captures sentiment dependencies between multiple aspects in a sentence.
- MHAGCN [21]: A model using hierarchical multi-head attention mechanisms and graph convolutional networks, thoroughly considering syntactic dependencies and combining semantic information to provide deep interaction among aspect terms and context.
- SS-GCN [14]: This model enhances semantic representation for aspect-level sentiment analysis through graph convolutional networks by automatically learning syntactic weight matrices and integrating syntactic and semantic information, thereby capturing aspect sentiment more accurately.
4.3.3. Multi-Task Joint Learning Model
- MNN [29]: Utilizes a unified sequence labeling scheme to define training tasks, simultaneously performing aspect term extraction and sentiment classification.
- LCF-ATEPC [30]: A multi-task learning model for Chinese ABSA that is capable of simultaneously extracting aspect terms and inferring their sentiment polarities.
- MTABSA [16]: Combines aspect term extraction and sentiment polarity classification in a multitask learning framework, leveraging multi-head attention and RGAT to capture key dependency relations and enhance classification performance.
- BLAB [17]: By integrating the AD-BiReGU module into the BERT-LCF framework, aspect term extraction and fine-grained sentiment analysis are performed simultaneously, addressing the limitation of existing models that primarily focus on a single task.
4.4. Main Results
4.5. Ablation Study
4.6. Impact of GCN Layers
4.7. Visualization of Attention Weights
4.8. Case Study
5. Conclusions and Future Work
5.1. Conclusions
5.2. Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
BERT | Bidirectional encoder representations from transformers |
ATE | Aspect term extraction |
SC | Sentiment polarity classification |
RGAT | Relational graph attention network |
Pos | Positive |
Neu | Neutral |
Neg | Negative |
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Dataset | Positive | Negative | Neutral | Total | |
---|---|---|---|---|---|
Restaurant 14 * | Train | 2164 | 807 | 637 | 3608 |
Test | 727 | 196 | 196 | 1119 | |
Laptop 14 * | Train | 937 | 851 | 455 | 2243 |
Test | 337 | 128 | 167 | 632 | |
Train | 1507 | 1528 | 3016 | 6051 | |
Test | 172 | 169 | 336 | 677 | |
MAMS | Train | 3380 | 2764 | 5042 | 11,186 |
valid | 403 | 325 | 604 | 1332 | |
Test | 400 | 329 | 607 | 1336 |
Hyper-Parameters | Value |
---|---|
word embedding dimension | 768 |
batch size | 12 |
learning rate | 2 × 10−5 |
training epochs | 20 |
dropout rate | 0.5 |
optimizer | Adam |
Models | Restaurant | Laptop | |
---|---|---|---|
F1ATE | F1ATE | F1ATE | |
DTBCSN (2017) | 83.97 | 75.66 | 75.33 |
RAL (2022) | 85.63 | 78.67 | 73.61 |
LDA (2022) | 81.00 | 75.00 | 74.00 |
DA-DCGCN (2024) | 87.61 | 82.74 | 83.42 |
MTL-GCN | 89.02 | 84.73 | 86.04 |
Models | Restaurant | Laptop | ||||
---|---|---|---|---|---|---|
Acc | F1SC | Acc | F1SC | Acc | F1SC | |
SDGCN-BERT (2020) | 83.57 | 76.47 | 81.35 | 78.34 | — | — |
DualGCN (2021) | 84.27 | 78.08 | 78.48 | 74.47 | 75.92 | 74.29 |
MHAGCN (2022) | 82.57 | 75.83 | 79.06 | 75.70 | 74.53 | 73.75 |
SS-GCN (2024) | 82.96 | 74.26 | 75.86 | 71.78 | — | — |
MTL-GCN | 89.49 | 84.35 | 81.62 | 78.23 | 81.53 | 80.22 |
Models | Restaurant | Laptop | MAMS | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
F1ATE | Acc | F1SC | F1ATE | Acc | F1SC | F1ATE | Acc | F1SC | F1ATE | Acc | F1SC | |
MNN (2018) | 83.05 | 77.17 | 68.50 | 76.94 | 70.40 | 65.98 | 72.05 | 71.05 | 63.87 | — | — | — |
LCF-ATEPC (2021) | 88.45 | 86.77 | 80.54 | 83.32 | 80.97 | 77.86 | 85.12 | 76.7 | 74.54 | — | — | — |
MTABSA (2023) | 87.45 | 86.88 | 81.16 | 81.55 | 80.56 | 77.00 | 87.33 | 76.21 | 74.34 | — | — | — |
BLAB (2024) | 89.47 | 88.46 | 82.79 | 84.57 | 80.45 | 78.02 | 88.87 | 79.39 | 79.28 | 83.72 | 84.56 | 85.37 |
MTL-GCN | 89.02 | 89.49 | 84.35 | 84.73 | 81.62 | 78.23 | 86.04 | 81.53 | 80.22 | 85.24 | 86.84 | 86.46 |
Methods | Restaurant | Laptop | MAMS | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
F1ATE | Acc | F1SC | F1ATE | Acc | F1SC | F1ATE | Acc | F1SC | F1ATE | Acc | F1SC | |
MTL-GCN | 89.02 | 89.49 | 84.35 | 84.73 | 81.62 | 78.23 | 86.04 | 81.53 | 80.22 | 85.24 | 86.84 | 86.46 |
P-GCN | 83.78 | 85.64 | 79.23 | 81.59 | 77.86 | 73.54 | 80.99 | 76.49 | 76.36 | 81.61 | 82.16 | 83.29 |
MHA | 87.99 | 87.92 | 82.85 | 83.75 | 80.84 | 77.31 | 84.39 | 79.62 | 78.76 | 84.07 | 85.93 | 84.61 |
Dep.tree | 85.13 | 86.91 | 80.05 | 82.53 | 78.19 | 76.26 | 83.62 | 79.76 | 78.51 | 82.01 | 84.87 | 84.23 |
Review Samples | Model | Predicted Label |
---|---|---|
The food is uniformly exceptional, with a very capable kitchen that will proudly whip up whatever you feel like eating, whether it’s on the menu or not. Label (Aspect: { food, kitchen, menu } Polarity: {pos, pos, neu}) | MTABSA | Aspect:{ food, kitchen, menu } Polarity:{pos, pos, neg} |
BLAB | Aspect:{ food, kitchen, menu } Polarity:{pos, pos, neu} | |
MTL-GCN | Aspect:{ food, kitchen, menu } Polarity: {pos, pos, neu} | |
The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the “sales” team, which is the retail shop from which I bought my netbook. Label(Aspect: { tech guy, service center, “sales” team } Polarity: {neu, neg, neg}) | MTABSA | Aspect: { service center, “sales” team } Polarity:{ neg, neu} |
BLAB | Aspect: { service center, “sales” team } Polarity:{ neg, neg} | |
MTL-GCN | Aspect: { tech guy, service center, “sales” team } Polarity:{neu, neg, neg} |
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Han, H.; Wang, S.; Qiao, B.; Dang, L.; Zou, X.; Xue, H.; Wang, Y. Aspect-Based Sentiment Analysis Through Graph Convolutional Networks and Joint Task Learning. Information 2025, 16, 201. https://doi.org/10.3390/info16030201
Han H, Wang S, Qiao B, Dang L, Zou X, Xue H, Wang Y. Aspect-Based Sentiment Analysis Through Graph Convolutional Networks and Joint Task Learning. Information. 2025; 16(3):201. https://doi.org/10.3390/info16030201
Chicago/Turabian StyleHan, Hongyu, Shengjie Wang, Baojun Qiao, Lanxue Dang, Xiaomei Zou, Hui Xue, and Yingqi Wang. 2025. "Aspect-Based Sentiment Analysis Through Graph Convolutional Networks and Joint Task Learning" Information 16, no. 3: 201. https://doi.org/10.3390/info16030201
APA StyleHan, H., Wang, S., Qiao, B., Dang, L., Zou, X., Xue, H., & Wang, Y. (2025). Aspect-Based Sentiment Analysis Through Graph Convolutional Networks and Joint Task Learning. Information, 16(3), 201. https://doi.org/10.3390/info16030201