Papers with Aspect-level sentiment classification
Transfer Capsule Network for Aspect Level Sentiment Classification (P19-1)
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| Challenge: | Lack of aspect-level labeled data is a major obstacle in sentiment classification due to high cost . document-level labels like reviews are easily accessible from online websites . |
| Approach: | They propose a transfer capsule network model for transferring document-level knowledge to aspect-level sentiment classification by encapsulating sentence-level semantic representations into semantic capsules. |
| Outcome: | The proposed model can transfer document-level knowledge to aspect-level sentiment classification. |
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. |
Attention Transfer Network for Aspect-level Sentiment Classification (2020.coling-main)
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| Challenge: | Aspect-level sentiment classification aims to detect the sentiment polarity of a given opinion target in a sentence. |
| Approach: | They propose a novel attention transfer network which can exploit attention from document-level sentiment datasets to improve the attention capability of the aspect-level classification task. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two ASC benchmark datasets. |
Effective Attention Modeling for Aspect-Level Sentiment Classification (C18-1)
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| Challenge: | Aspect-level sentiment classification aims to determine sentiment polarity of review sentence towards opinion target . main challenge is to separate different opinion contexts for different targets . |
| Approach: | They propose a method that captures the semantic meaning of the opinion target and a model that incorporates syntactic information into the attention mechanism. |
| Outcome: | The proposed method captures the semantic meaning of the opinion target and incorporates syntactic information into the attention mechanism. |
Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction (2021.findings-emnlp)
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| Challenge: | Aspect-level sentiment classification (ALSC) is a practical setting in aspect-based sentiment analysis due to no opinion term labeling needed, but it fails to interpret why a sentiment polarity is derived for the aspect. |
| Approach: | They propose a span-based anti-bias aspect representation learning framework that eliminates the sentiment bias in the aspect embedding by adversarial learning against aspects’ prior sentiment. |
| Outcome: | The proposed framework achieves state-of-the-art performance on five benchmarks, with the capability of unsupervised opinion extraction. |
Recognizing Conflict Opinions in Aspect-level Sentiment Classification with Dual Attention Networks (D19-1)
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| Challenge: | Existing models ignore conflict opinions because they are sparse in the datasets. |
| Approach: | They propose a multi-label classification model with dual attention mechanism to address these problems by excluding conflict opinions from existing models. |
| Outcome: | The proposed model addresses the problem of exclusion of conflict opinions from the datasets. |
Capsule Network with Interactive Attention for Aspect-Level Sentiment Classification (D19-1)
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| Challenge: | Existing methods for aspect-level sentiment classification are limited for dealing with overlapped features. |
| Approach: | They propose to use capsule network to construct vector-based feature representation and cluster features by an EM routing algorithm to model semantic relationship between aspect terms and context. |
| Outcome: | The proposed model achieves state-of-the-art on three datasets. |