Big Data Based Transfer Learning for Sentiment Classification with Multiple Source Domains
- 10.2991/978-94-6463-034-3_26How to use a DOI?
- Big Data; Transfer Learning; Sentiment Classification
Sentiment classification, served as a curial technology in natural language processing and computational linguistics, has drawn a lot of attentions from researchers. However, due to the high cost of manual labeling in the era of big data, conventional methods of sentiment classification are unqualified to be employed in a new domain directly. Hence, in this paper, we explore big data based transfer learning for sentiment classification with multiple source domains. To solve the problem of inherent domain gap, we propose a novel framework Adversarial cross-domain sentiment classification with weighted domain-dependent fEAture learning dubbed AdEA. Specifically, AdEA involves an individual domain-invariant feature extractor and several domain-dependent feature extractors. To obtain the domain-invariant feature, we use a reversed discriminator loss for these extractors. Furthermore, we propose a weighted learning module to reinforce the relationship of domain-dependent features between source and target domains. Integrated with these two domain-related features, AdEA is able to achieve better capability of cross-domain sentiment classification. Experimental results show the effectiveness of our proposed method.
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Cite this article
TY - CONF AU - Qing Li AU - Gang Wang AU - Guowu Yang PY - 2022 DA - 2022/12/23 TI - Big Data Based Transfer Learning for Sentiment Classification with Multiple Source Domains BT - Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022) PB - Atlantis Press SP - 256 EP - 265 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-034-3_26 DO - 10.2991/978-94-6463-034-3_26 ID - Li2022 ER -