Aspect-term level sentiment analysis (ATSA) is a fine-grained task in sentiment classification. It aims at extracting and summarizing the sentiment polarity towards a given aspect phrase from a sentence. Most existing studies combined various neural network models with a delicately carved attention mechanism to generate refined representations of sentences for better predictions. However, they were inadequate to capture correlations between aspects and sentiments. Moreover , the annotated aspect term might be unavailable in real-world scenarios which may challenge the existing methods to give correct forecasting . In this paper, we propose a capsule network based model named CAPSAR (CAPsule network with Sentiment-Aspect Reconstruction) to improve aspect-term level sentiment analysis. CAPSAR adopts a hierarchical structure of capsules and learns interactive patterns between aspects and sentiments through packaged sentiment-aspect reconstruction  . Capsules in CAPSAR are capable of communicating with other capsulesthrough a sharing-weight routing algorithm. Experiments on three ATSA benchmarks demonstrate the superiority of our model, and CAPSAR can detect the potential aspect terms from sentences by de-capsulizing the vectors in capsules when aspect terms are unknown .
Most recent existing aspect-term level sentiment analysis (ATSA) approaches combined various neural network models with delicately carved attention mechanisms built upon given aspect and context to generate refined sentence representations for better predictions. In these methods, aspect terms are always provided in both training and testing process which may degrade aspect-level analysis into sentence-level prediction. However , the annotated aspect term might be unavailable in real-world scenarios which may challenge the applicability of the existing methods . In this paper, we aim to improve ATSA by discovering the potential aspect terms of the predicted sentiment polarity when the aspect terms of a test sentence are unknown. We access this goal by proposing a capsule network based model named CAPSAR . In CAPSAR, sentiment categories are denoted by capsules and aspect term information is injected into sentiment capsules through a sentiment-aspect reconstruction procedure during the training. As a result, coherent patterns between aspects and sentimental expressions are encapsulated by these sentiment capsules . Experiments on three widely used benchmarks demonstrate these patterns have potential in exploring aspect terms from test sentence when only feeding the sentence to the model. Meanwhile, the proposed CAPSAR can clearly outperform SOTA methods in standard ATSA tasks .
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Aspect-term
Most recent existing aspect-term
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is a fine-grained task in sentiment classification. It aims at extracting and summarizing the sentiment polarity towards a given aspect phrase from a sentence. Most existing studies
approaches
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a
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mechanism
mechanisms built upon given aspect and context
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representations of sentences
sentence representations
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However, they were inadequate to capture correlations between aspects and sentiments. Moreover
In these methods, aspect terms are always provided in both training and testing process which may degrade aspect-level analysis into sentence-level prediction. However
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existing methods to give correct forecasting
applicability of the existing methods
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propose a
aim to improve ATSA by discovering the potential aspect terms of the predicted sentiment polarity when the aspect terms of a test sentence are unknown. We access this goal by proposing a
R
(CAPsule network with Sentiment-Aspect Reconstruction) to improve aspect-term level sentiment analysis. CAPSAR adopts a hierarchical structure of capsules and learns interactive patterns between aspects and sentiments through packaged
. In CAPSAR, sentiment categories are denoted by capsules and aspect term information is injected into sentiment capsules through a
A
procedure during the training. As a result, coherent patterns between aspects and sentimental expressions are encapsulated by these sentiment capsules
D
Capsules in CAPSAR are capable of communicating with other capsulesthrough a sharing-weight routing algorithm.
R
ATSA benchmarks demonstrate the superiority of our model, and CAPSAR can detect the potential
widely used benchmarks demonstrate these patterns have potential in exploring
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sentences by de-capsulizing the vectors in capsules when aspect terms are unknown
test sentence when only feeding the sentence to the model. Meanwhile, the proposed CAPSAR can clearly outperform SOTA methods in standard ATSA tasks