Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 4th International Conference on Signal Processing and Machine Learning

Series Vol. 52 , 27 March 2024


Open Access | Article

Sentiment analysis of Twitter user text based on the BERT model

Chenyang Zhou * 1
1 East China University of Technology

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 52, 102-108
Published 27 March 2024. © 27 March 2024 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Chenyang Zhou. Sentiment analysis of Twitter user text based on the BERT model. ACE (2024) Vol. 52: 102-108. DOI: 10.54254/2755-2721/52/20241380.

Abstract

Deep Neural Networks (DNNs) utilizing Recurrent Neural Network (RNN) architectures have found extensive application in text sentiment analysis. A prevailing notion suggests that augmenting the model's capacity can significantly improve accuracy and overall model performance. Building upon this premise, this paper advocates the adoption of a larger BERT model for text sentiment analysis. Bidirectional Encoder Representations from Transformers (BERT) is a sophisticated pre-trained language comprehension model that leverages Transformers as feature extractors. However, as the amount of model data increases, exceeding the memory limitations of a single GPU, algorithm optimization becomes crucial. Therefore, this paper employs two methods, namely data parallelism and GPipe parallelism, to accelerate and optimize the BERT model. Compared to a single GPU, training speed almost linearly increases with the addition of more GPUs. In addition, this research investigates the accuracy of the most advanced language model, chatgpt, by reannotating the dataset. During training, it was observed that the accuracy of the chatgpt-annotated dataset significantly declined in both RNN and BERT models. This indicates that chatgpt still exhibits some errors in sentiment text analysis.

Keywords

BERT, Sentiment analysis, Data optimization

References

1. Saon G Tüske Z Bolanos D et al 2021 Advancing RNN transducer technology for speech recognition. ICASSP 2021-2021 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) IEEE: 5654-5658

2. Yadav S P Zaidi S Mishra A et al 2022 Survey on machine learning in speech emotion recognition and vision systems using a recurrent neural network (RNN) Archives of Computational Methods in Engineering 29(3): 1753-1770

3. Prabha M I 2019 Srikanth G U. Survey of sentiment analysis using deep learning techniques 2019 1st international conference on innovations in information and communication technology (ICIICT). IEEE: 1-9

4. Von Oswald J Niklasson E Randazzo E et al 2023 Transformers learn in-context by gradient descent International Conference on Machine Learning PMLR: 35151-35174

5. Alaparthi S Mishra M 2020 Bidirectional Encoder Representations from Transformers (BERT): A sentiment analysis odyssey arXiv preprint arXiv:2007.01127

6. Devlin J Chang M W Lee K et al 2018 Bert: Pre-training of deep bidirectional transformers for language understanding arXiv preprint arXiv:1810.04805

7. Lin T Wang Y Liu X et al 2022 A survey of transformers AI Open

8. Huang Y Cheng Y Bapna A et al 2019 Gpipe: Efficient training of giant neural networks using pipeline parallelism Advances in neural information processing systems 32

9. Go A Bhayani R Huang L 2009 Twitter sentiment classification using distant supervision CS224N project report Stanford 1(12): 2009

10. Kaggle 2017 Sentiment140 dataset with 1.6 million tweets https://www.kaggle.com/datasets/kazanova/sentiment140

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 4th International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-83558-349-4
ISBN (Online)
978-1-83558-350-0
Published Date
27 March 2024
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/52/20241380
Copyright
27 March 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated