| Crynodeb: | Existing sentiment analysis algorithms mainly focus on vectorized textual data
representation and constructing high-quality deep learning classifiers. However,
improving sentence embedding methods could enhance textual sentiment classification
models. This project introduces a model for text-level sentiment classification
utilizing contrastive learning and BERT pre-trained language models.
Model combines SimCSE with self-supervised BERT training using contrastive
learning. It adapts a simple text level sentiment analysis dataset into pairs
through Back Translation, constructing siamese network BERTs. Each side of
these BERTs shares the same structure and parameters. By feeding sentiment
analysis text pairs generated through Back Translation into the BERT models,
sentence representation vectors are obtained. The model optimizes by summing
loss functions and back-propagating to improve performance. Finally, the onesided
BERT network from the trained siamese network BERTs is applied to the
supervised classification module for Chinese text sentiment classification.
Experimental validation on three Chinese datasets, including Waimai 10k, chnsenticorp
htl All, and online shopping 10 cats, demonstrates the effectiveness and
superiority of the model over several cutting-edge text-level sentiment classification
models.
Keywords: Natural language processing, Sentiment Analysis; Contrastive Learning;
Siamese Network.
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