Attention Modeling for Targeted Sentiment.
Jiangming Liu,Yue Zhang +1 more
- Vol. 2, pp 572-577
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TLDR
Results show that by using attention to model the contribution of each word in a sentence with respect to the target, this model gives significantly improved results over two standard benchmarks.Abstract:
Neural network models have been used for target-dependent sentiment analysis. Previous work focus on learning a target specific representation for a given input sentence which is used for classification. However, they do not explicitly model the contribution of each word in a sentence with respect to targeted sentiment polarities. We investigate an attention model to this end. In particular, a vanilla LSTM model is used to induce an attention value of the whole sentence. The model is further extended to differentiate left and right contexts given a certain target following previous work. Results show that by using attention to model the contribution of each word with respect to the target, our model gives significantly improved results over two standard benchmarks. We report the best accuracy for this task.read more
Citations
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Journal ArticleDOI
Deep learning for sentiment analysis: A survey
Lei Zhang,Shuai Wang,Bing Liu +2 more
TL;DR: Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results as mentioned in this paper, which is also popularly used in sentiment analysis in recent years.
Proceedings ArticleDOI
Multi-grained Attention Network for Aspect-Level Sentiment Classification
TL;DR: Experimental results show that the multi-grained attention network consistently outperforms the state-of-the-art methods on all three datasets, and the effectiveness of aspect alignment loss indicates the aspect-level interactions can bring extra useful information and further improve the performance.
Proceedings ArticleDOI
Transformation Networks for Target-Oriented Sentiment Classification
TL;DR: The authors proposed a new model that employs a CNN layer to extract salient features from the transformed word representations originated from a bi-directional RNN layer, which achieved state-of-the-art performance.
Proceedings ArticleDOI
Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks.
Chen Zhang,Qiuchi Li,Dawei Song +2 more
TL;DR: Wang et al. as discussed by the authors proposed a Graph Convolutional Network (GCN) over the dependency tree of a sentence to exploit syntactical information and word dependencies, and a novel aspect-specific sentiment classification framework was raised.
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Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks
Chen Zhang,Qiuchi Li,Dawei Song +2 more
TL;DR: This work proposes to build a Graph Convolutional Network (GCN) over the dependency tree of a sentence to exploit syntactical information and word dependencies and raises a novel aspect-specific sentiment classification framework.
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Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
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