Transfer Capsule Network for Aspect Level Sentiment Classification

By Zhuang Chen, Tieyun Qian. 2019

169 citations recorded by the source corpus.

Aspect-level sentiment classification aims to determine the sentiment polarity of a sentence towards an aspect. Due to the high cost in annotation, the lack of aspect-level labeled data becomes a major obstacle in this area. On the other hand, document-level labeled data like reviews are easily accessible from online websites. These reviews encode sentiment knowledge in abundant contexts. In this paper, we propose a Transfer Capsule Network (TransCap) model for transferring document-level knowledge to aspect-level sentiment classification. To this end, we first develop an aspect routing approach to encapsulate the sentence-level semantic representations into semantic capsules from both the aspect-level and document-level data. We then extend the dynamic routing approach to adaptively couple the semantic capsules with the class capsules under the transfer learning framework. Experiments on SemEval datasets demonstrate the effectiveness of TransCap.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Bing Liu
2. Hu Xu
3. Zixuan Ke
4. Erik Cambria
5. Philip S. Yu
6. Soujanya Poria
7. Dinesh Kumar Vishwakarma
8. Ashima Yadav

Citations in the corpus

• Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks (2021, methodology): “Recently, capsule networks (Hinton et al., 2011) have been used in sentiment classification and text classification (Chen and Qian, 2019; Zhao et al., 2019).”
• Sentiment analysis using deep learning architectures: a review (2019, methodology): “Sentiment analysis using deep learning architectures: a review 4365 They are also applicable for aspect-level sentiment classification (Du et al. 2019b; Chen and Qian 2019).”
• SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis (2020, background): “More recent approaches have been focusing on the development of sentiment-specific word embeddings [44], which are able to encode more affective clues than regular word vectors, and on the use of context-aware subsymbolic approaches such as attention modeling [32, 33] and capsule networks [13, 66].”
• Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks (2021, unknown): “• TransCap [23] transfers the sentiment features learned from document-level knowledge to aspect-level representations by adopting a transfer capsule network model.”
• A Survey on Text Classification: From Traditional to Deep Learning (2020, background): “TransCap [188] is proposed to encapsulate the sentence-level semantic representations into semantic capsules and transfer document-level knowledge.”
• A Survey on Text Classification: From Shallow to Deep Learning (2020, background): “TransCap [148] is proposed to encapsulate the sentence-level semantic representations into semantic capsules and transfer document-level knowledge.”
• Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification (2020, methodology): “We compare the proposed DGEDT∗ with a line of baselines and state-of-the-art alternatives, including LSTM, MemNet (Tang et al., 2016b), AOA (Huang et al., 2018), IAN (Ma et al., 2017), TNetLF (Li et al., 2018), CAPSNet (Chen and Qian, 2019), Transfer-CAPS (Chen and Qian, 2019), TGBERT (Gao et al., 2”