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. |
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| Challenge: | Existing methods to model relationships between aspects and opinion words are inefficient due to informal expressions and complexity of online reviews. |
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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. |
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| Challenge: | Existing methods for aspect sentiment analysis do not include explicit sentiment expressions. |
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SSEGCN: Syntactic and Semantic Enhanced Graph Convolutional Network for Aspect-based Sentiment Analysis (2022.naacl-main)
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| Challenge: | Aspect-based Sentiment Analysis (ABSA) aims to predict sentiment polarity towards aspects in sentences . a novel model for ABSA is proposed, but how to harness it is still a challenge . |
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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. |
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