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Graph Domain Adversarial Transfer Network for Cross-Domain Sentiment Classification
- Source :
- IEEE Access, Vol 9, Pp 33051-33060 (2021)
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- In the text sentiment classification task, some words are seemingly unrelated to the classification task, but they have a direct impact on the performance of classification model. For example, in the sentences “I have terminal cancer” and “Cancer is a very common disease”, it can be clearly found that the word “cancer” has two different sentiment tendencies in the daily life domain and the medical domain. In the daily life domain, the word “cancer” shows an extremely negative sentiment tendency. While in the medical domain, the word “cancer” is just a simple term with a relatively neutral sentiment tendency. Although current deep learning models have already achieved good performance through their powerful feature learning capabilities, there are serious deficiencies in dealing with the above problem. Therefore, from a new perspective, this paper proposes the Graph Domain Adversarial Transfer Network (GDATN) based on the idea of adversarial learning, which uses the labeled source domain data to predict the sentiment label of unlabeled target domain data. Firstly, GDATN extracts feature representations through the Bidirectional Long Short-Term Memory (BiLSTM) Network and Graph Attention Network (GAT) successively. Then, GDATN introduces the domain classifier to capture the domain-shared text feature representation with the Gradient Reversal Layer (GRL). In addition, an auxiliary task named the projection mechanism is constructed to further capture the domain-specific text feature representation in response to the text domain problem. Extensive experimental results on two benchmark datasets show that GDATN proposed in this paper outperforms the other six benchmark sentiment classification models, and GDATN has a better stability on different cross-domain pairs.
- Subjects :
- General Computer Science
Computer science
Feature extraction
Stability (learning theory)
02 engineering and technology
010501 environmental sciences
computer.software_genre
01 natural sciences
Domain (software engineering)
projection mechanism
Classifier (linguistics)
0202 electrical engineering, electronic engineering, information engineering
Feature (machine learning)
General Materials Science
0105 earth and related environmental sciences
business.industry
Deep learning
Adversarial transfer learning
General Engineering
cross-domain sentiment classification
Graph (abstract data type)
020201 artificial intelligence & image processing
lcsh:Electrical engineering. Electronics. Nuclear engineering
Artificial intelligence
business
lcsh:TK1-9971
computer
Feature learning
Natural language processing
gradient reversal layer
Subjects
Details
- ISSN :
- 21693536
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....54dcfdfef45c116ddcd443bcf8cbe0a1