Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree
By Kai Sun, Richong Zhang, Samuel Mensah, Yongyi Mao, Xudong Liu. Conference on Empirical Methods in Natural Language Processing, 2019
377 citations recorded by the source corpus.
We propose a method based on neural networks to identify the sentiment polarity of opinion words expressed on a specific aspect of a sentence. Although a large majority of works typically focus on leveraging the expressive power of neural networks in handling this task, we explore the possibility of integrating dependency trees with neural networks for representation learning. To this end, we present a convolution over a dependency tree (CDT) model which exploits a Bi-directional Long Short Term Memory (Bi-LSTM) to learn representations for features of a sentence, and further enhance the embeddings with a graph convolutional network (GCN) which operates directly on the dependency tree of the sentence. Our approach propagates both contextual and dependency information from opinion words to aspect words, offering discriminative properties for supervision. Experimental results ranks our approach as the new state-of-the-art in aspect-based sentiment classification.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/ca352ea6ac66c03a0cc22759098713e4202c71c6
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In Artificial Intelligence
Citations in the corpus
- SentiLARE: Linguistic Knowledge Enhanced Language Representation for Sentiment Analysis (2019, methodology): “…DRNN (Wang, 2018), ML (Sachan et al., 2019) for sentence-level sentiment classification, DE-CNN (Xu et al., 2018) for aspect term extraction, CDT (Sun et al., 2019a) for aspect term sentiment classification, TAN (Movahedi et al., 2019) for aspect category detection, and AS-Capsules (Wang et al.,…”
- Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis (2021, methodology): “Following this line, a few works extend the GCN and GAT models by means of a syntactical dependency tree and develop several outstanding models (Zhang et al., 2019; Sun et al., 2019b; Huang and Carley, 2019; Wang et al., 2020; Tang et al., 2020).”
- A Survey of Human-in-the-loop for Machine Learning (2021, background): “Recently numerous neural network-based approaches have been widely utilized and demonstrated their effectiveness in solving sentiment analysis tasks [79, 103, 104, 105].”
- Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks (2021, positive): “graph networks based on dependency tree are proposed to model the structure of a sentence through the dependency tree of the sentence [10, 11].”
- Relational Graph Attention Network for Aspect-based Sentiment Analysis (2020, background): “Zhang et al. (2019) and Sun et al. (2019b) proposed to use graph convolutional networks (GCN) to learn node representations from a dependency tree and used them together with The [falafel] neg was over cooked and dried but the [chicken] pos was fine.”