Vietnamese Sentiment Analysis under Limited Training Data Based on Deep Neural Networks

The annotated dataset is an essential requirement to develop an artificial intelligence (AI) system effectively and expect the generalization of the predictive models and to avoid overfitting. Lack of the training data is a big barrier so that AI systems can broaden in several domains which have no...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2022, Vol.2022 (1)
Main Authors: Duong, Huu-Thanh, Nguyen-Thi, Tram-Anh, Hoang, Vinh Truong
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Data augmentation
Data mining
Datasets
Deep learning
Domains
Evaluation
Experiments
Fault diagnosis
Language
Languages
Machine learning
Morphology
Natural language processing
Neural networks
Prediction models
Sentiment analysis
Training
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Summary:The annotated dataset is an essential requirement to develop an artificial intelligence (AI) system effectively and expect the generalization of the predictive models and to avoid overfitting. Lack of the training data is a big barrier so that AI systems can broaden in several domains which have no or missing training data. Building these datasets is a tedious and expensive task and depends on the domains and languages. This is especially a big challenge for low-resource languages. In this paper, we experiment and evaluate many various approaches on sentiment analysis problems so that they can still obtain high performances under limited training data. This paper uses the preprocessing techniques to clean and normalize the data and generate the new samples from the limited training dataset based on many text augmentation techniques such as lexicon substitution, sentence shuffling, back translation, syntax-tree transformation, and embedding mixup. Several experiments have been performed for both well-known machine learning-based classifiers and deep learning models. We compare, analyze, and evaluate the results to indicate the advantage and disadvantage points of the techniques for each approach. The experimental results show that the data augmentation techniques enhance the accuracy of the predictive models; this promises that smart systems can be applied widely in several domains under limited training data.
ISSN:1076-2787
1099-0526
DOI:10.1155/2022/3188449