Aspect Based Sentiment Analysis with Gated Convolutional Networks

By Wei Xue, Tao Li. 2018

611 citations recorded by the source corpus.

Aspect based sentiment analysis (ABSA) can provide more detailed information than general sentiment analysis, because it aims to predict the sentiment polarities of the given aspects or entities in text. We summarize previous approaches into two subtasks: aspect-category sentiment analysis (ACSA) and aspect-term sentiment analysis (ATSA). Most previous approaches employ long short-term memory and attention mechanisms to predict the sentiment polarity of the concerned targets, which are often complicated and need more training time. We propose a model based on convolutional neural networks and gating mechanisms, which is more accurate and efficient. First, the novel Gated Tanh-ReLU Units can selectively output the sentiment features according to the given aspect or entity. The architecture is much simpler than attention layer used in the existing models. Second, the computations of our model could be easily parallelized during training, because convolutional layers do not have time dependency as in LSTM layers, and gating units also work independently. The experiments on SemEval datasets demonstrate the efficiency and effectiveness of our models.

Subfields: Artificial Intelligence

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Citations in the corpus

Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning (2021, methodology): “For ASC, we adopt the ASC classification network in [67], which takes aspect term and review sentence as input.”
Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks (2021, methodology): “We adopt the ASC classi-ﬁcation network in (Xue and Li, 2018), which takes both aspect term and review sentence as input.”
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks (2021, methodology): “work in (Xue and Li, 2018), taking aspect term and review sentence as input for BERT variants.”
Position-Aware Tagging for Aspect Sentiment Triplet Extraction (2020, background): “Such a task either refers to predicting sentiment polarity for a given target (Dong et al., 2014; Chen et al., 2017; Xue and Li, 2018; Wang and Lu, 2018; Wang et al., 2018; Li et al., 2018a; Peng et al., 2018; Xu et al., 2020) or joint extraction of targets as well as sentiment associated with each ”
Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks (2021, positive): “• GCAE [17] uses a gated CNN model to selectively control the propagation of the sentiment features with respect to the given aspect.”
Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree (2019, methodology): “Most recent methods have focused on leveraging neural networks (Chen et al., 2017; Gu et al., 2018; Majumder et al., 2018; Fan et al., 2018a; Xue and Li, 2018; Huang and Carley, 2018; Zheng and Xia, 2018) which model representations automatically.”
Target-Dependent Sentiment Classification With BERT (2019, background): “The results with ‘‘∗’’ are directly taken from SemEval-2014 [28], ‘‘\’’ from GCAE [47].”
Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks (2019, methodology): “In addition to the attention-based models, Convolutional Neural Networks (CNNs) (Xue and Li, 2018; Li et al., 2018) have been employed to discover descriptive multi-word phrases for an aspect, based on the ﬁnding (Fan et al., 2018) that the sentiment of an aspect is usually determined by key…”
A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis (2019, methodology): “…2016a), AT LSTM (Wang et al., 2016), ATAE LSTM (Wang et al., 2016), BiLSTM enhanced with attention mechanism, IAN (Ma et al., 2017) and AOA LSTM (Huang et al., 2018); (2) CNN based model: GCAE (Xue and Li, 2018); (3) Attention based models: Mem-Net (Tang et al., 2016b) and AEN (Song et al., 2019).”
Attention, please! A survey of neural attention models in deep learning (2021, neutral): “(2017), Xue and Li (2018), Feng et al.”
Exploiting BERT for End-to-End Aspect-based Sentiment Analysis (2019, background): “…al., 2016; Wang et al., 2016; Chen et al., 2017; Liu and Zhang, 2017; Ma et al., 2017, 2018b; Majumder et al., 2018; Li et al., 2018; He et al., 2018; Xue and Li, 2018; Wang et al., 2018; Fan et al., 2018; Huang and Carley, 2018; Lei et al., 2019; Li et al., 2019b; Zhang et al., 2019) 2 , but the…”
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, methodology): “In the combination of CNNs and RNNs, ConvNet-RNN networks with the cascaded fusion [230,283] or the ensemble strategy [215] have been proposed for FER, by simultaneously capturing textural and temporal information.”
Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification (2020, result): “CNN based attention methods (Xue and Li, 2018; Li et al., 2018) are also proposed to enhance the phrase-level representation and achieved encouraging results.”
Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks (2019, background): “, 2018) and few of them use convolutional neural networks (CNN) (Huang and Carley, 2018; Xue and Li, 2018).”
Relational Graph Attention Network for Aspect-based Sentiment Analysis (2020, methodology): “• Other recent methods: GCAE (Xue and Li, 2018), JCI (Wang et al., 2018) and TNET (Li et al., 2018).”
Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis (2019, background): “The ‘Sentiment’ circle linked to aspect terms refers to ATC which attracts a heated research popularity (Dong et al. 2014; Tang, Qin, and Liu 2016; Nguyen and Shirai 2015; Wang et al. 2016b; Ma et al. 2017; Tay, Luu, and Hui 2017; Ma, Peng, and Cambria 2018; Hazarika et al. 2018; Li et al. 2018a; Wa”
A Unified Generative Framework for Aspect-based Sentiment Analysis (2021, methodology): “, 2018a; Xue and Li, 2018), gated neural network (Zhang et al., 2016; Xue and Li, 2018), memory neural”
Transfer Capsule Network for Aspect Level Sentiment Classification (2019, background): “Settings We use Glove vectors with 840B to-kens (Pennington et al., 2014) Compared Methods To demonstrate the superiority of our TransCap for ASC tasks, we compare it with followings baselines: ATAE-LSTM (Wang et al., 2016b), IAN (Ma et al., 2017), AF-LSTM(CONV) (Tay et al., 2018), AF-LSTM(CORR) (Ta”