Abstract
The COVID-19 pandemic has changed the way we go about our everyday lives, and we will continue to see its impact for a long time. These changes especially apply to the business world, where the market is very volatile as a result. Requirements of the people are changing rapidly, as are the restrictions on transport and trade of goods. Due to the intense competition and struggles brought about due to the pandemic, acting first on profit opportunities is crucial to businesses doing well in the current climate. Thus, getting the relevant news in time, out of the huge number of COVID-19 related articles published daily is of utmost importance. The same applies to other industries, like the medical industry, where innovations and solutions to managing COVID-19 can save lives, and money in other parts of the world. Manually combing through the massive number of articles posted every day is both impractical and laborious. This task has the potential to be automated using Natural Language Processing (NLP) with Deep Learning based approaches. In this paper, we conduct exhaustive experiments to find the best combination of word-embedding, feature selection, and classification techniques; and find the best structure for the Deep Learning model for article classification in the COVID-19 context.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anowar, F., Sadaoui, S., Selim, B.: Conceptual and empirical comparison of dimensionality reduction algorithms (pca, kpca, lda, mds, svd, lle, isomap, le, ica, t-sne). Comput. Sci. Rev. 40, 100378 (2021)
Beltagy, I., Cohan, A., Lo, K.: Scibert: pretrained contextualized embeddings for scientific text. arXiv preprint arXiv:1903.10676 vol. 1, no. 1.3, p. 8 (2019)
Cai, J., Li, J., Li, W., Wang, J.: Deeplearning model used in text classification. In: 2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pp. 123–126 (2018). https://doi.org/10.1109/ICCWAMTIP.2018.8632592
Deng, X., Li, Y., Weng, J., Zhang, J.: Feature selection for text classification: a review. Multimedia Tools Appl. 78(3), 3797–3816 (2018). https://doi.org/10.1007/s11042-018-6083-5
Dong, S., Wang, P., Abbas, K.: A survey on deep learning and its applications. Comput. Sci. Rev. 40, 100379 (2021)
Gupta, H., Kulkarni, T.G., Kumar, L., Neti, L.B.M., Krishna, A.: An empirical study on predictability of software code smell using deep learning models. In: Barolli, L., Woungang, I., Enokido, T. (eds.) AINA 2021. LNNS, vol. 226, pp. 120–132. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75075-6_10
Ji, H., Qin, W., Yuan, Z., Meng, F.: Qualitative and quantitative recognition method of drug-producing chemicals based on sno2 gas sensor with dynamic measurement and pca weak separation. Sens. Actuators, B Chem. 348, 130698 (2021)
Kalouptsoglou, I., Siavvas, M., Kehagias, D., Chatzigeorgiou, A., Ampatzoglou, A.: An Empirical evaluation of the usefulness of word embedding techniques in deep learning-based vulnerability prediction. In: Gelenbe, E., Jankovic, M., Kehagias, D., Marton, A., Vilmos, A. (eds.) Security in Computer and Information Sciences, EuroCybersec 2021, Communications in Computer and Information Science, vol. 1596, pp. 23–37. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-09357-9_3
Khadhraoui, M., Bellaaj, H., Ammar, M.B., Hamam, H., Jmaiel, M.: Survey of bert-base models for scientific text classification: Covid-19 case study. Appl. Sci. 12(6) (2022). https://doi.org/10.3390/app12062891, https://www.mdpi.com/2076-3417/12/6/2891
Kumar, L., Baldwa, S., Jambavalikar, S.M., Murthy, L.B., Krishna, A.: Software functional and non-function requirement classification using word-embedding. In: Barolli, L., Hussain, F., Enokido, T. (eds.) AINA 2022. LNNS, vol. 450, pp. 167–179. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-99587-4_15
Kumar, L.: Deep-learning approach with DeepXplore for software defect severity level prediction. In: Gervasi, O., et al. (eds.) ICCSA 2021. LNCS, vol. 12955, pp. 398–410. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87007-2_28
Kumar, L., Kumar, M., Murthy, L.B., Misra, S., Kocher, V., Padmanabhuni, S.: An empirical study on application of word embedding techniques for prediction of software defect severity level. In: 2021 16th Conference on Computer Science and Intelligence Systems (FedCSIS), pp. 477–484. IEEE (2021)
Li, Q., et al.: A survey on text classification: from traditional to deep learning. ACM Trans. Intell. Syst. Technol. 13(2) (2022). https://doi.org/10.1145/3495162
Li, X., et al.: Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond (2021). https://doi.org/10.48550/ARXIV.2103.10689, https://arxiv.org/abs/2103.10689
Liu, Q., Wang, L.: t-test and Anova for data with ceiling and/or floor effects. Behav. Res. Methods 53(1), 264–277 (2021)
Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., Gao, J.: Deep learning-based text classification: a comprehensive review. ACM Comput. Surv. 54(3) (2021). https://doi.org/10.1145/3439726
Mirończuk, M.M., Protasiewicz, J.: A recent overview of the state-of-the-art elements of text classification. Exp. Syst. Appl. 106, 36–54 (2018). https://doi.org/10.1016/j.eswa.2018.03.058, https://www.sciencedirect.com/science/article/pii/S095741741830215X
Nguyen, H.N., Teerakanok, S., Inomata, A., Uehara, T.: The comparison of word embedding techniques in RNNs for vulnerability detection. In: ICISSP, pp. 109–120 (2021)
Niu, Z., Zhong, G., Yu, H.: A review on the attention mechanism of deep learning. Neurocomputing 452, 48–62 (2021)
Selva Birunda, S., Kanniga Devi, R.: A review on word embedding techniques for text classification. In: Raj, J.S., Iliyasu, A.M., Bestak, R., Baig, Z.A. (eds.) Innovative Data Communication Technologies and Application. LNDECT, vol. 59, pp. 267–281. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-9651-3_23
Shorten, C., Khoshgoftaar, T.M., Furht, B.: Deep learning applications for covid-19. J. Big Data 8(1), 1–54 (2021)
Tummalapalli, S., Kumar, L., Murthy Neti, L.B., Kocher, V., Padmanabhuni, S.: A novel approach for the detection of web service anti-patterns using word embedding techniques. In: Gervasi, O., et al. (eds.) ICCSA 2021. LNCS, vol. 12955, pp. 217–230. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87007-2_16
Tummalapalli, S., Kumar, L., Neti, L.B.M., Krishna, A.: Detection of web service anti-patterns using weighted extreme learning machine. Comput. Stand. Interfaces 82, 103621 (2022)
Wang, R., Li, Z., Cao, J., Chen, T., Wang, L.: Convolutional recurrent neural networks for text classification. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1–6 (2019). https://doi.org/10.1109/IJCNN.2019.8852406
Yu, Z., Guindani, M., Grieco, S.F., Chen, L., Holmes, T.C., Xu, X.: Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research. Neuron 110(1), 21–35 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Vijayvargiya, S., Kumar, L., Murthy, L.B., Misra, S. (2022). COVID-19 Article Classification Using Word-Embedding and Different Variants of Deep-Learning Approach. In: Florez, H., Gomez, H. (eds) Applied Informatics. ICAI 2022. Communications in Computer and Information Science, vol 1643. Springer, Cham. https://doi.org/10.1007/978-3-031-19647-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-19647-8_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19646-1
Online ISBN: 978-3-031-19647-8
eBook Packages: Computer ScienceComputer Science (R0)