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01.12.2023 | Original Article

BERT-deep CNN: state of the art for sentiment analysis of COVID-19 tweets

verfasst von: Javad Hassannataj Joloudari, Sadiq Hussain, Mohammad Ali Nematollahi, Rouhollah Bagheri, Fatemeh Fazl, Roohallah Alizadehsani, Reza Lashgari, Ashis Talukder

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2023

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Abstract

The COVID-19 pandemic has led to the emergence of social media platforms as crucial channels for the dissemination of information and public opinion. Comprehending the sentiment conveyed in tweets on COVID-19 is of paramount importance for individuals involved in policymaking, crisis management, and public health administration. This study seeks to conduct a comprehensive review of the current BERT and deep CNN models utilized in sentiment analysis of COVID-19 tweets. Additionally, the study aims to propose potential future research directions for the development of a BERT model that is both lightweight and high quality. The BERT model acquires contextual representations of words and effectively captures the intricate semantics of tweets related to COVID-19, whereas the deep CNN captures the hierarchical organization of the tweet embeddings. The performance of the model is exceptional, exceeding the current sentiment analysis methods for tweets related to COVID-19. Our study involves a comprehensive analysis of vast COVID-19 tweet datasets, wherein we establish the efficacy of the BERT-deep CNN models in precisely categorizing the sentiment of COVID-19 tweets in real time. The outcomes of the research offer significant perspectives on the public's attitudes, supporting decision-makers in comprehending the general viewpoint, detecting disinformation, and guiding emergency response tactics. Additionally, this study serves to enhance the progress of sentiment analysis methodologies within the realm of public health emergencies and establishes a standard for forthcoming investigations in the sentiment analysis of social media data pertaining to COVID-19.

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Abdelminaam DS, Ismail FH, Taha M, Taha A, Houssein EH, Nabil A (2021) Coaid-deep: an optimized intelligent framework for automated detecting covid-19 misleading information on twitter. IEEE Access 9:27840–27867

Anand S et al. (2019) Suggestion mining from online reviews using ulmfit, arXiv preprint arXiv:1904.09076

Babu YP, Eswari R (2020) CIA_NITT at WNUT-2020 task 2: classification of COVID-19 tweets using pre-trained language models, arXiv preprint arXiv:2009.05782

Bansal V, Tyagi M, Sharma R, Gupta V, Xin Q (2022) A transformer based approach for abuse detection in code mixed indic languages. In: ACM transactions on Asian and low-resource language information processing

Basiri ME, Nemati S, Abdar M, Asadi S, Acharrya UR (2021) A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets. Knowl-Based Syst 228:107242

Baziotis C, Pelekis N, Doulkeridis C (2017) Datastories at semeval-2017 task 4: Deep lstm with attention for message-level and topic-based sentiment analysis. In: Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017), pp 747–754

Biswas E, Karabulut ME, Pollock L, Vijay-Shanker K (2020) Achieving reliable sentiment analysis in the software engineering domain using bert. In: 2020 IEEE international conference on software maintenance and evolution (ICSME), IEEE, pp 162–173

Carvalho J, Plastino A (2021) On the evaluation and combination of state-of-the-art features in Twitter sentiment analysis. Artif Intell Rev 54(3):1887–1936

Chakraborty K, Bhatia S, Bhattacharyya S, Platos J, Bag R, Hassanien AE (2020) Sentiment analysis of COVID-19 tweets by deep learning classifiers—a study to show how popularity is affecting accuracy in social media. Appl Soft Comput 97:106754

Chandra R, Krishna A (2021) COVID-19 sentiment analysis via deep learning during the rise of novel cases. PLoS ONE 16(8):e0255615

Chatsiou K (2020) Text classification of COVID-19 press briefings using BERT and convolutional neural networks

Chaturvedi I, Cambria E, Welsch RE, Herrera F (2018) Distinguishing between facts and opinions for sentiment analysis: Survey and challenges. Inf Fusion 44:65–77

Chen N, Zhong Z, Pang J (2021) An exploratory study of COVID-19 information on twitter in the greater region. Big Data Cogn Comput 5(1):5

Chintalapudi N, Battineni G, Amenta F (2021) Sentimental analysis of COVID-19 tweets using deep learning models. Infect Dis Rep 13(2):329–339

D’Andrea E, Ducange P, Bechini A, Renda A, Marcelloni F (2019) Monitoring the public opinion about the vaccination topic from tweets analysis. Expert Syst Appl 116:209–226

Dai Z, Yang Z, Yang Y, Carbonell J, Le QV, Salakhutdinov R (2019) Transformer-xl: Attentive language models beyond a fixed-length context, arXiv preprint arXiv:1901.02860

de Heras-Pedrose CL, Sánchez-Núñez P, Peláez JI (2020) Sentiment analysis and emotion understanding during the COVID-19 pandemic in Spain and its impact on digital ecosystems. Int J Environ 17(15):5542

Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding, arXiv preprint arXiv:1810.04805

Dhingra S, Arora R, Katariya P, Kumar A, Gupta V, Jain N (2021) Understanding emotional health sustainability amidst COVID-19 imposed lockdown. In: Sustainability measures for COVID-19 Pandemic, pp 211–235

Feng X, Liu X (2019) Sentiment classification of reviews based on BiGRU neural network and fine-grained attention. In: Journal of Physics: Conference Series, vol 1229, no 1: IOP Publishing, p 012064

Garcia K, Berton L (2021) Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA. Appl Soft Comput 101:107057

Gencoglu O (2020) Large-scale, language-agnostic discourse classification of tweets during COVID-19. Mach Learn Knowl Extr 2(4):603–616

Glazkova A, Glazkov M, Trifonov T (2021) g2tmn at constraint@ aaai2021: exploiting CT-BERT and ensembling learning for COVID-19 fake news detection. International Workshop on Combating On line Ho st ile Posts in Regional Languages dur ing Emerge ncy Si tuation. Springer, Berlin, pp 116–127

Gupta V et al (2021a) An emotion care model using multimodal textual analysis on COVID-19. Chaos Solitons Fractals 144:110708

Gupta V, Dass P, Arora R (2022) Pendulating or resonating? a case of echo-chambers in twitter. J Discrete Math Sci Cryptogr 25(1):231–240

Gupta V, Jain N, Shubham S, Madan A, Chaudhary A, Xin Q (2021b) Toward integrated cnn-based sentiment analysis of tweets for scarce-resource language—Hindi. Trans Asian Low-Resource Lang Inf Process 20(5):1–23

Gupta V, Singh VK, Mukhija P, Ghose U (2019) Aspect-based sentiment analysis of mobile reviews. J Intell Fuzzy Syst 36(5):4721–4730

Hayawi K, Shahriar S, Serhani MA, Taleb I, Mathew SS (2022) ANTi-Vax: a novel Twitter dataset for COVID-19 vaccine misinformation detection. Public Health 203:23–30

Hernández-García A, König P (2018) Data augmentation instead of explicit regularization, arXiv preprint arXiv:1806.03852

Hossain T, Logan IV RL, Ugarte A, Matsubara Y, Young S, Singh S (2020) COVIDLies: detecting COVID-19 misinformation on social media

Houlsby N et al. (2019) Parameter-efficient transfer learning for NLP. In: International conference on machine learning: PMLR, pp 2790–2799

Hutto C, Gilbert E (2014) Vader: a parsimonious rule-based model for sentiment analysis of social media text. Proc Int AAAI Conf Web Soc Media 8(1):216–225

Jalil Z et al (2020) A novel benchmark dataset for COVID-19 detection during third wave in Pakistan. Comput Intell Neurosci 2022:25741

Jalil Z et al (2021) Covid-19 related sentiment analysis using state-of-the-art machine learning and deep learning techniques. Public Health Front 9:4158MathSciNet

Jelodar H, Wang Y, Orji R, Huang S (2020) Deep sentiment classification and topic discovery on novel coronavirus or COVID-19 online discussions: NLP using LSTM recurrent neural network approach. IEEE J Biomed Health Inform 24(10):2733–2742

Jiang H, He P, Chen W, Liu X, Gao J, Zhao T (2019) Smart: Robust and efficient fine-tuning for pre-trained natural language models through principled regularized optimization, arXiv preprint arXiv:1911.03437

Kabir MY, Madria S (2021) EMOCOV: machine learning for emotion detection, analysis and visualization using COVID-19 tweets. Online Soc Netw Media 23:100135

Kairon P, Bhattacharyya S (2021) COVID-19 outbreak prediction using quantum neural networks. In: Intelligence enabled research: Springer, pp 113–123

Kruspe A, Häberle M, Kuhn I, Zhu XX (2020) Cross-language sentiment analysis of european twitter messages duringthe covid-19 pandemic. arXiv preprint arXiv:2008.12172

Lan Z, Chen M, Goodman S, Gimpel K, Sharma P, Soricut R (2019) Albert: a lite bert for self-supervised learning of language representations, arXiv preprint arXiv:1909.11942

Lee J et al (2020) BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4):1234–1240MathSciNet

Ling J (2020) Coronavirus public sentiment analysis with BERT deep learning. Springer, Berlin

Liu Y et al. (2019) Roberta: a robustly optimized bert pretraining approach, arXiv preprint arXiv:1907.11692

Malla S, Alphonse P (2021) COVID-19 outbreak: an ensemble pre-trained deep learning model for detecting informative tweets. Appl Soft Comput 107:107495

Martínez-Cámara E, Martín-Valdivia MT, Urena-López LA, Montejo-Ráez AR (2014) Sentiment analysis in Twitter. Nat Lang Eng 20(1):1–28

Mehmood M, Rizwan M, Abbas S (2021) Machine learning assisted cervical cancer detection. Public Health Front 2:2024

Munikar M, Shakya S, Shrestha A (2019) Fine-grained sentiment classification using BERT. In: 2019 artificial intelligence for transforming business and society (AITB), vol 1: IEEE, pp 1–5

Müller M, Salathé M, Kummervold PE (2020) Covid-twitter-bert: A natural language processing model to analyse covid-19 content on twitter, arXiv preprint arXiv:2005.07503

Nair AJ, Veena G, Vinayak A (2021) Comparative study of twitter sentiment on covid-19 tweets. In: 2021 5th international conference on computing methodologies and communication (ICCMC), IEEE, pp 1773–1778

Naseem U, Razzak I, Khushi M, Eklund PW, Kim J (2021) COVIDSenti: a large-scale benchmark Twitter data set for COVID-19 sentiment analysis. IEEE Trans Comput Soc Syst 8(4):1003–1015

Nguyen DQ, Vu T, Nguyen AT (2020) BERTweet: a pre-trained language model for English Tweets, arXiv preprint arXiv:2005.10200

Pham HT, Rafieizonooz M, Han S, Lee D-E (2021) Current status and future directions of deep learning applications for safety management in construction. Sustainability 13(24):13579

Pokharel BP (2020) Twitter sentiment analysis during covid-19 outbreak in nepal, Available at SSRN 3624719

Pota M, Esposito M, Palomino MA, Masala GL (2018) A subword-based deep learning approach for sentiment analysis of political tweets. In: 2018 32nd international conference on advanced information networking and applications workshops (WAINA), IEEE, pp 651–656

Radford A, Narasimhan K, Salimans T, Sutskever I (2018) Improving language understanding by generative pre-training. Springer, Berlin

Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I (2019) Language models are unsupervised multitask learners. OpenAI Blog 1(8):9

Rogers A, Kovaleva O, Rumshisky A (2020) A primer in bertology: What we know about how bert works. Trans Assoc Comput Linguist 8:842–866

Rustam F, Khalid M, Aslam W, Rupapara V, Mehmood A, Choi GS (2021) A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis. PLoS ONE 16(2):e0245909

Saadah S, Auditama KM, Fattahila AA, Amorokhman FI, Aditsania A, Rohmawati AA (2022) Implementation of BERT, IndoBERT, and CNN-LSTM in Classifying Public Opinion about COVID-19 Vaccine in Indonesia. Jurnal RESTI (rekayasa Sistem Dan Teknologi Informasi) 6(4):648–655

Sachin S, Tripathi A, Mahajan N, Aggarwal S, Nagrath P (2020) Sentiment analysis using gated recurrent neural networks. SN Comput Sci 1(2):1–13

Sadia K, Basak S (2021b) Sentiment analysis of COVID-19 tweets: how does BERT perform? In: Proceedings of international joint conference on advances in computational intelligence, Springer, pp 407–416

Shah AM, Yan X, Tariq S, Khan S (2021) Listening to the patient voice: using a sentic computing model to evaluate physicians’ healthcare service quality for strategic planning in hospitals. Qual Quant 55(1):173–201

Shahi T, Sitaula C, Paudel N (2022) A hybrid feature extraction method for Nepali COVID-19-related tweets classification. Comput Intell Neurosci 2022:552

Shawe-Taylor J, Cristianini N (2000) An introduction to support vector machines and other kernel-based learning methods

Sitaula C, Basnet A, Mainali A, Shahi TB (2021) Deep learning-based methods for sentiment analysis on Nepali covid-19-related tweets. Comput Intell Neurosci 2021:85

Sitaula C, Shahi TB (2022) Multi-channel CNN to classify nepali covid-19 related tweets using hybrid features, arXiv preprint arXiv:2203.10286

Song X, Petrak J, Jiang Y, Singh I, Maynard D, Bontcheva K (2021) Classification aware neural topic model for COVID-19 disinformation categorisation. PLoS ONE 16(2):e0247086

Song Y, Wang J, Liang Z, Liu Z, Jiang T (2020) Utilizing BERT intermediate layers for aspect based sentiment analysis and natural language inference, arXiv preprint arXiv:2002.04815

Sun C, Qiu X, Xu Y, Huang X (2019) How to fine-tune bert for text classification? China national conference on Chinese computational linguistics. Springer, Berlin, pp 194–206

To QG et al (2021) Applying machine learning to identify anti-vaccination tweets during the COVID-19 pandemic. Int J Environ Res Public Health 18(8):4069

Tziafas G, Kogkalidis K, Caselli T (2021) Fighting the COVID-19 infodemic with a holistic BERT ensemble, arXiv preprint arXiv:2104.05745

Valdes A, Lopez J, Montes M (2021) UACH-INAOE at SMM4H: a BERT based approach for classification of COVID-19 Twitter posts. In: Proceedings of the sixth social media mining for health (# SMM4H) Workshop and Shared Task, pp 65–68

Vishwamitra N, Hu RR, Luo F, Cheng L, Costello M, Yang Y (2020) On analyzing covid-19-related hate speech using bert attention. In: 2020 19th IEEE international conference on machine learning and applications (ICMLA), IEEE, pp 669–676

Yang Z, Dai Z, Yang Y, Carbonell J, Salakhutdinov RR, Le QV (2019) Xlnet: generalized autoregressive pretraining for language understanding. In: Advances in neural information processing systems, vol 32

Zhang Y, Lyu H, Liu Y, Zhang X, Wang Y, Luo J (2020) Monitoring depression trend on Twitter during the COVID-19 pandemic, arXiv preprint arXiv:2007.00228

Zhou J, Yang S, Xiao C, Chen F (2021) Examination of community sentiment dynamics due to COVID-19 pandemic: a case study from a state in Australia. SN Comput Sci 2:1–11

Titel: BERT-deep CNN: state of the art for sentiment analysis of COVID-19 tweets
verfasst von: Javad Hassannataj Joloudari
Sadiq Hussain
Mohammad Ali Nematollahi
Rouhollah Bagheri
Fatemeh Fazl
Roohallah Alizadehsani
Reza Lashgari
Ashis Talukder
Publikationsdatum: 01.12.2023
Verlag: Springer Vienna
Erschienen in: Social Network Analysis and Mining / Ausgabe 1/2023
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-023-01102-y

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