Multilingual sentiment analysis during the pandemic using deep learning models

Research Article
Open access

Multilingual sentiment analysis during the pandemic using deep learning models

Yichen Chang 1*
  • 1 Xi’an Jiaotong-Liverpool University    
  • *corresponding author yichen.chang19@student.xjtlu.edu.cn
Published on 26 February 2024 | https://doi.org/10.54254/2755-2721/43/20230847
ACE Vol.43
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-311-1
ISBN (Online): 978-1-83558-312-8

Abstract

High mood swings and increased mortality with the COVID-19 outbreak, mood changes are a concern. COVID-19 is extremely contagious, and by December 20, 2021, the global death toll from COVID-19 exceeded 5.35 million. Under such circumstances, people changed their original way of life and isolated themselves from home to work online. Most people in this situation expressed their emotions through social media. In order to understand the changes of people's emotions during the pandemic, this research analyzed the emotions of people's messages posted on social media in different countries through the SenWave dataset by different deep learning models. The dataset contains 6 languages (English, Arabic, Spanish, French, Italian and Chinese) with more than 70,000 tweets, in this paper, only tweets collected from Twitter are concerned, which contains 11 labels. The dataset will be preprocessed and transferred into numerical vector, then TextCNN, BERT, and BERT-TextCNN models are implemented for the above problems. BERT model reached 0.69 and 0.53 on precision and recall respectively on Twitter dataset.

Keywords:

Multilingual sentiment analysis, multi-label classification, deep learning

Chang,Y. (2024). Multilingual sentiment analysis during the pandemic using deep learning models. Applied and Computational Engineering,43,284-293.
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References

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[29]. Q. Lv, W. Liu, R. Li, H. Yang, Y. Tao, and M. Wang, ‘Classification of Seismaesthesia Information and Seismic Intensity Assessment by Multi-Model Coupling’, ISPRS Int. J. Geo-Inf., vol. 12, no. 2, Art. no. 2, Feb. 2023, doi: 10.3390/ijgi12020046.

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Cite this article

Chang,Y. (2024). Multilingual sentiment analysis during the pandemic using deep learning models. Applied and Computational Engineering,43,284-293.

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 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-311-1(Print) / 978-1-83558-312-8(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.43
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. G. Zammarchi, F. Mola, and C. Conversano, ‘Using sentiment analysis to evaluate the impact of the COVID-19 outbreak on Italy’s country reputation and stock market performance’, Stat. Methods Appl., Apr. 2023, doi: 10.1007/s10260-023-00690-5.

[2]. Y. Madani, M. Erritali, and B. Bouikhalene, ‘A new sentiment analysis method to detect and Analyse sentiments of Covid-19 moroccan tweets using a recommender approach’, Multimed. Tools Appl., pp. 1–20, Feb. 2023, doi: 10.1007/s11042-023-14514-x.

[3]. ‘Feeling Positive About Reopening? New Normal Scenarios From COVID-19 US Reopen Sentiment Analytics | IEEE Journals & Magazine | IEEE Xplore’. https://ieeexplore.ieee.org/document/9154672 (accessed Sep. 15, 2023).

[4]. ‘Deep Learning Model for COVID-19 Sentiment Analysis on Twitter | SpringerLink’. https://link.springer.com/article/10.1007/s00354-023-00209-2 (accessed Sep. 16, 2023).

[5]. ‘Studying the psychology of coping negative emotions during COVID-19: a quantitative analysis from India | SpringerLink’. https://link.springer.com/article/10.1007/s11356-021-16002-x (accessed Sep. 16, 2023).

[6]. ‘Trends and prevalence of suicide 2017-2021 and its association with COVID-19: Interrupted time series analysis of a national sample of college students in the United States - PubMed’. https://pubmed.ncbi.nlm.nih.gov/35987067/ (accessed Sep. 16, 2023).

[7]. ‘SemEval-2018 Task 1: Affect in Tweets - ACL Anthology’. https://aclanthology.org/S18-1001/ (accessed Sep. 16, 2023).

[8]. A. Law and A. Ghosh, ‘Multi-Label Classification Using Binary Tree of Classifiers’, IEEE Trans. Emerg. Top. Comput. Intell., vol. 6, no. 3, pp. 677–689, Jun. 2022, doi: 10.1109/TETCI.2021.3075717.

[9]. K. Machova, M. Mach, and M. Vasilko, ‘Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data’, Sensors, vol. 22, no. 1, Art. no. 1, Jan. 2022, doi: 10.3390/s22010155.

[10]. I. Lasri, A. Riadsolh, and M. Elbelkacemi, ‘Real-time Twitter Sentiment Analysis for Moroccan Universities using Machine Learning and Big Data Technologies’, Int. J. Emerg. Technol. Learn. IJET, vol. 18, pp. 42–61, Mar. 2023, doi: 10.3991/ijet.v18i05.35959.

[11]. ‘Environmental Complaint Text Classification Scheme Combining Automatic Annotation and TextCNN | IEEE Conference Publication | IEEE Xplore’. https://ieeexplore.ieee.org/abstract/document/9727661 (accessed Sep. 18, 2023).

[12]. B. Guo, C. Zhang, J. Liu, and X. Ma, ‘Improving text classification with weighted word embeddings via a multi-channel TextCNN model’, Neurocomputing, vol. 363, pp. 366–374, Oct. 2019, doi: 10.1016/j.neucom.2019.07.052.

[13]. Y. Cao, Z. Sun, L. Li, and W. Mo, ‘A Study of Sentiment Analysis Algorithms for Agricultural Product Reviews Based on Improved BERT Model’, Symmetry, vol. 14, no. 8, Art. no. 8, Aug. 2022, doi: 10.3390/sym14081604.

[14]. T. Kumar, M. Mahrishi, and G. Sharma, ‘Emotion recognition in Hindi text using multilingual BERT transformer’, Multimed. Tools Appl., Apr. 2023, doi: 10.1007/s11042-023-15150-1.

[15]. X. Zhang and Y. Ma, ‘An ALBERT-based TextCNN-Hatt hybrid model enhanced with topic knowledge for sentiment analysis of sudden-onset disasters’, Eng. Appl. Artif. Intell., vol. 123, p. 106136, Aug. 2023, doi: 10.1016/j.engappai.2023.106136.

[16]. A. F. Abdillah, C. B. P. Putra, A. Apriantoni, S. Juanita, and D. Purwitasari, ‘Ensemble-based Methods for Multi-label Classification on Biomedical Question-Answer Data’, J. Inf. Syst. Eng. Bus. Intell., vol. 8, no. 1, Art. no. 1, Apr. 2022, doi: 10.20473/jisebi.8.1.42-50.

[17]. S. Srivastava, B. Paul, and D. Gupta, ‘Study of Word Embeddings for Enhanced Cyber Security Named Entity Recognition’, Procedia Comput. Sci., vol. 218, pp. 449–460, Jan. 2023, doi: 10.1016/j.procs.2023.01.027.

[18]. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, ‘BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding’. arXiv, May 24, 2019. doi: 10.48550/arXiv.1810.04805.

[19]. B. Heinzerling and M. Strube, ‘BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages’. arXiv, Oct. 05, 2017. Accessed: Apr. 23, 2023. [Online]. Available: http://arxiv.org/abs/1710.02187

[20]. ‘Rise and fall of the global conversation and shifting sentiments during the COVID-19 pandemic | Humanities and Social Sciences Communications’. https://www.nature.com/articles/s41599-021-00798-7 (accessed Oct. 31, 2022).

[21]. Q. Yang et al., ‘SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic’. arXiv, Jun. 18, 2020. doi: 10.48550/arXiv.2006.10842.

[22]. J. Qiang, Y. Li, Y. Zhu, Y. Yuan, and X. Wu, ‘Lexical Simplification with Pretrained Encoders’. arXiv, Oct. 28, 2020. Accessed: Apr. 16, 2023. [Online]. Available: http://arxiv.org/abs/1907.06226

[23]. E. Çelik and T. Dalyan, ‘Unified benchmark for zero-shot Turkish text classification’, Inf. Process. Manag., vol. 60, no. 3, p. 103298, May 2023, doi: 10.1016/j.ipm.2023.103298.

[24]. Y. Kim, ‘Convolutional Neural Networks for Sentence Classification’. arXiv, Sep. 02, 2014. Accessed: Apr. 23, 2023. [Online]. Available: http://arxiv.org/abs/1408.5882

[25]. B. Lu, Z. Lin, and X. Zhang, ‘Text Analysis Method Based on Multi-channel Parallel Classifier’, in 2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE), Mar. 2022, pp. 6–12. doi: 10.1109/ICICSE55337.2022.9828968.

[26]. X. Jiang, C. Song, Y. Xu, Y. Li, and Y. Peng, ‘Research on sentiment classification for netizens based on the BERT-BiLSTM-TextCNN model’, PeerJ Comput. Sci., vol. 8, p. e1005, Jun. 2022, doi: 10.7717/peerj-cs.1005.

[27]. ‘torch.nn — PyTorch 2.0 documentation’. https://pytorch.org/docs/stable/nn.html (accessed Apr. 23, 2023).

[28]. J. Deng and F. Ren, ‘Multi-Label Emotion Detection via Emotion-Specified Feature Extraction and Emotion Correlation Learning’, IEEE Trans. Affect. Comput., vol. 14, no. 1, pp. 475–486, Jan. 2023, doi: 10.1109/TAFFC.2020.3034215.

[29]. Q. Lv, W. Liu, R. Li, H. Yang, Y. Tao, and M. Wang, ‘Classification of Seismaesthesia Information and Seismic Intensity Assessment by Multi-Model Coupling’, ISPRS Int. J. Geo-Inf., vol. 12, no. 2, Art. no. 2, Feb. 2023, doi: 10.3390/ijgi12020046.

[30]. H.-D. Le, G.-S. Lee, S.-H. Kim, S. Kim, and H.-J. Yang, ‘Multi-Label Multimodal Emotion Recognition With Transformer-Based Fusion and Emotion-Level Representation Learning’, IEEE Access, vol. 11, pp. 14742–14751, 2023, doi: 10.1109/ACCESS.2023.3244390.

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