Relational Graph Attention Network for Aspect-based Sentiment Analysis (2020.acl-main)
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| Challenge: | Aspect-based sentiment analysis aims to determine the sentiment polarity towards a specific aspect in online reviews. |
| Approach: | They propose a relational graph attention network to encode a tree structure for sentiment prediction. |
| Outcome: | The proposed approach improves the performance of the graph attention network (GAT) on the SemEval 2014 and Twitter datasets. |
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Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks (D19-1)
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| Challenge: | Aspect level sentiment classification aims to identify the sentiment expressed towards an aspect given a context sentence. |
| Approach: | They propose a target-dependent graph attention network for aspect level sentiment classification . it explicitly utilizes the dependency relationship among words to propagate sentiment features . they show that using BERT representations further substantially boosts the performance . |
| Outcome: | The proposed method outperforms baselines with GloVe embeddings and improves with BERT representations. |
Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification (2020.coling-main)
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| Challenge: | Existing approaches to aspect-level sentiment classification focus on modeling the relationship between aspect words and their contexts with attention, and ignore the use of elaborate knowledge implicit in the context. |
| Approach: | They exploit syntactic awareness to the model by the graph attention network on the dependency tree structure and external pre-training knowledge by BERT language model, which helps to model the interaction between the context and aspect words better. |
| Outcome: | The proposed model can model the interaction between the context and aspect words better by using syntactic awareness and external pre-training knowledge. |
BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis (2022.findings-acl)
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| Challenge: | Aspect-based sentiment analysis is challenging because a sentence may contain multiple aspects or complicated relationships. |
| Approach: | They propose a bi-syntax aware Graph Attention Network to model the context of every aspect and sentiment relations across aspects for learning. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on four benchmark datasets. |
Discrete Opinion Tree Induction for Aspect-based Sentiment Analysis (2022.acl-long)
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| Challenge: | Dependency trees are used for aspect-based sentiment classification but are not optimized for aspect classification. |
| Approach: | They propose an aspect-specific and language-agnostic discrete latent opinion tree model as an alternative structure to explicit dependency trees. |
| Outcome: | The proposed model can achieve competitive performance and interpretability on six English benchmarks and one Chinese dataset. |
Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks (D19-1)
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| Challenge: | Existing aspects-based sentiment classification models lack a mechanism to account for relevant syntactical constraints and word dependencies. |
| Approach: | They propose to build a Graph Convolutional Network over the dependency tree of a sentence to exploit syntactical information and word dependencies. |
| Outcome: | The proposed model is comparable to state-of-the-art models on three benchmarking collections. |
Graph Attention Network with Memory Fusion for Aspect-level Sentiment Analysis (2020.aacl-main)
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| Challenge: | Recent studies ignored the syntactic relationship between the aspect and its corresponding context words, leading the model to focus on syntaktically unrelated words mistakenly. |
| Approach: | They propose to extend the graph convolutional network by assigning different weights to edges of connected words. |
| Outcome: | The proposed method can improve on five datasets showing that it learns and exploits multiword relations and draws different weights of words to improve performance. |
Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation (2020.findings-emnlp)
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Amir Pouran Ben Veyseh, Nasim Nouri, Franck Dernoncourt, Quan Hung Tran, Dejing Dou, Thien Huu Nguyen
| Challenge: | Aspect-based Sentiment Analysis (ABSA) seeks to predict sentiment polarity of input sentences toward a specific aspect. |
| Approach: | They propose a graph-based deep learning model that integrates dependency trees into deep learning models to improve ABSA performance. |
| Outcome: | The proposed model achieves state-of-the-art on three benchmark datasets. |
Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree (D19-1)
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| Challenge: | Existing methods to identify sentiment polarity of opinion words are cumbersome due to the amount of opinionated material on the internet. |
| Approach: | They propose a method to identify sentiment polarity of opinion words on a specific aspect of a sentence using neural networks. |
| Outcome: | The proposed method is the state-of-the-art in aspect-based sentiment classification. |
Aspect-based Sentiment Analysis with Type-aware Graph Convolutional Networks and Layer Ensemble (2021.naacl-main)
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| Challenge: | Existing studies only leverage dependency relations without considering their dependency types . a valid and effective approach is demonstrated on six English benchmark datasets . |
| Approach: | They propose to explicitly utilize dependency types for ABSA with type-aware graph convolutional networks . attention is used in T-GCN to distinguish different edges in the graph and attentive layer ensemble to comprehensively learn from different layers of T-gCN. |
| Outcome: | The proposed approach performs well on six English benchmark datasets. |
Inducing Target-Specific Latent Structures for Aspect Sentiment Classification (2020.emnlp-main)
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| Challenge: | Aspect-level sentiment analysis aims to classify the sentiment polarity of an aspect or a target in a comment . graph convolutional networks can be used to classifice aspect terms in syllables . |
| Approach: | They propose to combine word dependency graphs and latent graphs to create latent models . they propose to model the interaction between the aspect and its surrounding contexts . |
| Outcome: | The proposed model can complement syntactic features with latent semantic dependencies. |