Competitive Capsule Network Based Sentiment Analysis on Twitter COVID’19 Vaccines

Authors

  • V. Diviya Prabha Department of Computer Science, Periyar University, Periyar Palkalai Nagar, Salem – 636011, Tamil Nadu, India https://orcid.org/0000-0002-0956-7316
  • R. Rathipriya Department of Computer Science, Periyar University, Periyar Palkalai Nagar, Salem – 636011, Tamil Nadu, India

DOI:

https://doi.org/10.13052/jwe1540-9589.2159

Keywords:

Data Mining, Sentiment Analysis, Capsule Network, Twitter, deep learning, COVID-19, machine language

Abstract

COVID-19 is an extremely contagious virus that has rapidly spread around the world. This disease has infected people of all ages in India, from children to the elderly. Vaccination, on the other hand, is the only way to preserve human lives. In the midst of a pandemic, it’s critical to know what people think of COVID-19 immunizations. The primary goal of this article is to examine corona vaccination tweets from India’s Twitter social media. This study introduces CompCapNets, a unique deep learning approach for Twitter sentiment classification. The results suggest that the proposed method outperforms other strategies when compared to existing traditional methods.

Downloads

Download data is not yet available.

Author Biographies

V. Diviya Prabha, Department of Computer Science, Periyar University, Periyar Palkalai Nagar, Salem – 636011, Tamil Nadu, India

V. Diviya Prabha has completed a Ph.D in Data Mining at the Department of Computer Science, Periyar University, Salem in the year 2022. She did her M.Phil (Computer Science) and M.C.A at the same university in the years of 2019 and 2013. She has published several research papers in international journals.

R. Rathipriya, Department of Computer Science, Periyar University, Periyar Palkalai Nagar, Salem – 636011, Tamil Nadu, India

R. Rathipriya is an Assistant Professor in the Department of Computer Science at Periyar University, Salem, Tamil Nadu. She received her M. Sc., M. Phil., and MCA degrees from the same Periyar University. She was awarded a Ph.D. at Bharathiyar University, Tamil Nadu, India. She has published several research papers in international journals. She is an expert in Web Mining and has acquired solid experience in Bioinformatics. Her research areas are bio-inspired computing techniques and bio-informatics.

References

Agarwal, B.; Nayak, R.; Mittal, N.; Patnaik, S (2020) .Deep Learning-Based Approaches for Sentiment Analysis, XII, 319, Springer.

Amr Mousa and Björn Schuller. 2017. Contextual bidirectional long short-term memory recurrent neural network language models: A generative approach to sentiment analysis.In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, volume 1, pages 1023–1032.

Cach N. Dang.; María N.; Moreno-García.; and Fernando De la Prieta (2021). Hybrid Deep Learning Models for Sentiment Analysis, Hindawi

Cai, G.; Xia, B. Convolutional neural networks for multimedia sentiment analysis. In Proceedings of the 4th CCF Conference on Natural Language Processing and Chinese Computing—Volume 9362; Springer-Verlag: Berlin/Heidelberg, Germany, 2015; pp. 159–167.

Cheng, J.; Dong, L.; Lapata, M. (2016). Long short-term memory-networks for machine reading. arXiv:1601.06733.

Diviya Prabha, V.; Rathipriya, R (2013). Biclustering of web usage data using Gravitational Search Algorithm, International Conference on Pattern Recognition, Informatics and Mobile Engineering, IEEE.

Diviya Prabha, V.; Rathipriya, R (2021) Sentimental Analysis using Capsule Network with Gravitational Search Algorithm, Journal of Web Engineering.

Gaye B , Zhang D and Wulamu A., (2021) A Tweet Sentiment Classification Approach Using a Hybrid Stacked Ensemble Technique, MDPI, Information.

Haftu Wedajo Fentaw and Tae-Hyong Kim, Design and Investigation of Capsule Networks for Sentence Classification,Applied Science, 9, 2200, 2019.

Harleen Kaur, Shafqat Ul Ahsaan, Bhavya Alankar ,Victor Chang (2021). A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets, Information Systems Frontiers, Springer.

Hermann, K.M.; Blunsom, P. The Role of Syntax in Vector Space Models of Compositional Semantics. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, Sofia, Bulgaria, 4–9 August 2013.

Hinton, G.E.; Krizhevsky, A.; Wang, S.D. Transforming auto-encoders. In Artificial Neural Networks and Machine Learning—CANN 2011; Honkela, T., Duch, W., Girolami, M., Kaski, S., Eds.; Springer: Berlin/Heidelberg, Germany, 2011.

Kalchbrenner, N.; Grefenstette, E.; Blunsom, P. A convolutional neural network for modelling sentences. arXiv 2014, arXiv:1404.2188.

Khattak A. M., Batool R,; Satti , F. A. et al., (2020). Tweets classification and sentiment analysis for personalized tweets recommendation,” Complexity, vol. 2020, pages 202.

Kim, J.; Jang, S.; Choi, S.; Park, E.L. Text classification using capsules. arXiv 2018, arXiv:1808.03976.

Kim, Y. Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, 25–29 October 2014; pp. 1746–1751.

McCallum, A., Nigam, K., et al. (1998). A comparison of event models for naive bayes text classification. In AAAI-98 workshop on learning for text categorization, Vol. 752 (pp. 41–48). Citeseer.

Pang, B.; Lee, L.; Vaithyanathan, S. Thumbs up?: Sentiment classification using machine learning techniques.In Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing—Volume 10; Association for Computational Linguistics: Stroudsburg, PA, USA, 2002; pp. 79–86.

R.Janani and S. Vijayarani, Automatic text classification using machine learning and optimization algorithms,Soft Computing,Springer, Vol. 25, Pages 1129–1145, 2020.

Sabour, S.; Frosst, N.; Hinton, G.E. Dynamic routing between capsules. In Advances in Neural Information Processing Systems; Neural Information Processing Systems Foundation, Inc.: Long Beach, CA, USA, 2017.

Samya R, Rathipriya R, (2016). Predictive analysis for weather prediction using data mining with ANN: a study, International Journal of Computational Intelligence and Informatics.

Silva, J.; Coheur, L.; Mendes, A.C.; Wichert, A. From symbolic to sub-symbolic information in question classification. Artif. Intell. Rev. 2011, 35, 137–154.

Tripathy, A.; Agrawal, A; Rath, S.K. Classification of sentiment reviews using n-gram machine learning approach, Expert System Appl. 2016, 57, 117–126.

Xian Zhong, Jinhang Liu, Shuqin Chen. (2020). An emotion classification algorithm based on SPT-CapsNet, Deep Learning and Neutral Computing for Intelligent Sensing and Control, Neural Computing and Applications.

Zhang, X.; LeCun, Y. Text understanding from scratch. arXiv 2015, arXiv:1502.01710.

Zhao, W.; Ye, J.; Yang, M.; Lei, Z.; Zhang, S.; Zhao, Z. Investigating capsule networks with dynamic routing for text classification. arXiv 2018, arXiv:1804.00538.

Published

2022-08-08

Issue

Section

Articles