Evaluation of semi-supervised data augmentation in sentence sentiment analysis

Gaoyuan Wang

doi:10.1117/12.2655897

12 January 2023 Evaluation of semi-supervised data augmentation in sentence sentiment analysis

Gaoyuan Wang

Proceedings Volume 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022); 125091W (2023) https://doi.org/10.1117/12.2655897
Event: Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 2022, Guangzhou, China

Abstract

Data augmentation by using Semi-Supervised Learning is an important research direction currently. Usually, this method applies the possible score of prediction as the confidence degree for the next training iteration so that Semi-Supervised Learning may gradually enhance the model's accuracy. The principle of Semi-Supervised Learning is using the original model to predict the unlabeled data to produce pseudo labels, then choosing the high confidence pseudo label to train the model iteratively. However, this method may not be helpful due to the iterative steps. This paper uses the model in this iteration to predict the data labeled in the last iteration and compares the pseudo label identity with the result produced by the previous iteration model. The evaluation shows that the consistency between models in two consecutive iterations is not high, which explains that Semi-Supervised Learning does not eventually enhance the model's accuracy. This research indicates that applying consistency and accuracy jointly as the standard for Semi-Supervised Learning in sentence sentiment analysis is a more reasonable method.

Citation Download Citation

Gaoyuan Wang "Evaluation of semi-supervised data augmentation in sentence sentiment analysis", Proc. SPIE 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 125091W (12 January 2023); https://doi.org/10.1117/12.2655897

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