| 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. |
Similar Papers
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. |
Attention and Lexicon Regularized LSTM for Aspect-based Sentiment Analysis (P19-2)
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| Challenge: | End-to-end deep learning systems lack flexibility as one cannot adjust the network to fix an obvious problem. |
| Approach: | They propose a way to leverage lexicon information to make the model more flexible . they also explore the effect of regularizing attention vectors to allow the network to have a broader "focus" |
| Outcome: | The proposed approach leverages lexicon information to make it more flexible and robust. |
Exploiting Document Knowledge for Aspect-level Sentiment Classification (P18-2)
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| Challenge: | Existing public aspect-level datasets for aspect-based sentiment classification are small . existing methods for aspect level sentiment classification require annotation of all opinion targets . |
| Approach: | They propose two approaches that transfer knowledge from document-level data to improve aspect-level sentiment classification. |
| Outcome: | The proposed methods improve aspect-level sentiment classification on 4 public datasets. |
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. |
Multi-grained Attention Network for Aspect-Level Sentiment Classification (D18-1)
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| Challenge: | Existing approaches to aspect sentiment classification use coarse-grained attention mechanisms . a novel approach captures word-level interaction between aspect and context . |
| Approach: | They propose a novel multi-grained attention network model for aspect level sentiment classification . they use a fine-grounded attention mechanism to capture word-level interaction between aspect and context . |
| Outcome: | The proposed model outperforms the state-of-the-art methods on three datasets . it shows that aspect-level interactions can bring extra useful information and improve performance . |
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. |
Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis (P19-1)
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| Challenge: | Experimental results show that our proposed approach yields better attention mechanisms . dominant ASC models are mostly discriminative classifiers based on manual feature engineering . |
| Approach: | They propose a self-supervised approach to aspect-level sentiment classification that mines useful attention supervision information from a training corpus to refine attention mechanisms. |
| Outcome: | The proposed approach yields better attention mechanisms on multiple datasets. |
Target-Sensitive Memory Networks for Aspect Sentiment Classification (P18-1)
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| Challenge: | Aspect sentiment classification (ASC) is a fundamental task in sentiment analysis. |
| Approach: | They propose to use memory networks to deal with ASC using aspect and sentence terms and use them to classify the sentiment polarity. |
| Outcome: | The proposed techniques can be implemented in a variety of contexts and their effectiveness is evaluated. |
Aspect Sentiment Classification with Aspect-Specific Opinion Spans (2020.emnlp-main)
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| Challenge: | Existing attention-based models for sentiment analysis are not able to capture opinion spans as a whole or variable-length opinion span. |
| Approach: | They propose a model that extracts aspect-specific opinion spans and evaluates sentiment polarity by exploiting extracted opinion features. |
| Outcome: | The proposed model extracts aspect-specific opinion spans and evaluates sentiment polarity using extracted opinion features. |
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. |