Skip to main content
SpringerLink
Log in

Sentiment Analysis of Twitter Data for COVID-19 Posts

  • Conference paper
  • First Online:
Data Intelligence and Cognitive Informatics

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

COVID-19 (Coronavirus Disease of 2019) is now a genuine danger to the health of the world’s population. Vaccines capable of halting this pandemic have received much scientific and financial attention. These days expressing emotions over any social media platform is becoming a trend. This paper aims to study a technique known as sentiment analysis to learn about the sentiments of an individual. It takes a keyword as an input and will give you the polarity (positive or negative) as an output. Tweets can be positive for some and harmful for others. In order to get insight into how people perceive a particular tweet, the necessary steps are performed over a set of data. There is a tremendous amount of data available on the web for internet users, and much more data is being created as online technology continues to progress and flourish. The Internet has evolved into a medium for online education, idea exchange, and the expression of personal views. Twitter, Facebook, and Google + are among the most popular social networking sites because they allow users to share and express their opinions on various issues, engage with individuals from all backgrounds, and send messages worldwide. Sentiment analysis of Twitter data has gotten much attention. As a result, this survey focuses primarily on sentiment analysis of Twitter data, which helps analyze information in highly unstructured, varied, and neutral tweets. Sentiment analysis on Twitter has been explored as a whole and specific issues and uses. Some people favor getting vaccinations, while others are against them. This has led to much controversy among individuals. This study aims to develop a framework model for categorizing the sentiment and opinions expressed in tweets about the COVID-19 vaccinations. In order to better understand the people’s views on vaccines and focus their efforts on them, public health organizations need to identify such viewpoints. SVM and NB are two machine learning classification models employed here. Pre-processing approaches for unstructured tweets were also utilized.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Liu C-L, Hsaio W-H, Lee C-H, Lu G-C, Jou E (2011) Movie rating and review summarization in mobile environment. IEEE Trans Syst Man Cybern Part C (Appl Rev) 42(3):397–407

    Google Scholar 

  2. Diviya M, Karmel A (2022) Review on technological advancement and textual data management algorithms in NLP and CBIR systems. In: Artificial intelligence and technologies. Springer, pp 311–321

    Google Scholar 

  3. Luo Y, Huang W (2011) Product review information extraction based on adjective opinion words. In: 2011 Fourth international joint conference on computational sciences and optimization, pp 1309–1313

    Google Scholar 

  4. Alabid NN, Katheeth ZD (2021) Sentiment analysis of twitter posts related to the COVID-19 vaccines. Indones J Electr Eng Comput Sci 24(3):1727–1734

    Google Scholar 

  5. Gautam G, Yadav D (2014) Sentiment analysis of twitter data using machine learning approaches and semantic analysis. In: 2014 Seventh international conference on contemporary computing (IC3), pp 437–442

    Google Scholar 

  6. Gowda SR, Archana BR, Shettigar P, Satyarthi KK (2022) Sentiment analysis of twitter data using Naïve Bayes classifier. In: ICDSMLA 2020. Springer, pp 1227–1234

    Google Scholar 

  7. Shuaib M et al (2022) Identity model for blockchain-based land registry system: a comparison. Wirel Commun Mob Comput 2022:1–17. https://doi.org/10.1155/2022/5670714

    Article  Google Scholar 

  8. Shuaib M et al (2022) Land registry framework based on self-sovereign identity (SSI) for environmental sustainability. Sustainability 14(9):5400

    Article  Google Scholar 

  9. Bhatia S, Alam S, Shuaib M, Alhameed MH, Jeribi F, Alsuwailem RI (2022) Retinal vessel extraction via assisted multi-channel feature map and U-net. Front Public Heal 10

    Google Scholar 

  10. Bharany S et al (2021) Energy-efficient clustering scheme for flying ad-hoc networks using an optimized LEACH protocol. Energies 14(19):6016

    Article  Google Scholar 

  11. Shuaib M et al (2022) Self-sovereign identity solution for blockchain-based land registry system: a comparison. Mob Inf Syst 2022:1–17. https://doi.org/10.1155/2022/8930472

    Article  Google Scholar 

  12. Bharany S et al (2022) A systematic survey on energy-efficient techniques in sustainable cloud computing. Sustainability 14(10):6256

    Article  Google Scholar 

  13. Rahmani MKI et al (2022) Blockchain-based trust management framework for cloud computing-based internet of medical things (IoMT): a systematic review. Comput Intell Neurosci 2022

    Google Scholar 

  14. Alam S et al (2021) Blockchain-based initiatives: current state and challenges. Comput Netw 198. https://doi.org/10.1016/j.comnet.2021.108395

  15. Khubrani MM, Alam S (2021) A detailed review of blockchain-based applications for protection against pandemic like COVID-19. 19(4):1185–1196. https://doi.org/10.12928/TELKOMNIKA.v19i4.18465.

  16. Nisha KA, Kulsum U, Rahman S, Hossain M, Chakraborty P, Choudhury T (2022) A comparative analysis of machine learning approaches in personality prediction using MBTI. In: Computational intelligence in pattern recognition. Springer, pp 13–23

    Google Scholar 

  17. Kalaivani MS, Jayalakshmi S (2022) Text-based sentiment analysis with classification techniques—a state-of-art study. In: Computer networks and inventive communication technologies. Springer, pp 277–285

    Google Scholar 

  18. Reegu FA, Daud SM, Alam S, Shuaib M (2021) Blockchain-based electronic health record system for efficient covid-19 pandemic management. https://doi.org/10.20944/preprints202104.0771.v1

  19. Rajalakshmi S, Asha S, Pazhaniraja N (2017) A comprehensive survey on sentiment analysis. In: 2017 Fourth international conference on signal processing, communication and networking (ICSCN), pp 1–5

    Google Scholar 

  20. Talwar B, Arora A, Bharany S (2021) An energy efficient agent aware proactive fault tolerance for preventing deterioration of virtual machines within cloud environment. In: 2021 9th International conference on reliability, infocom technologies and optimization (Trends and Future Directions) (ICRITO), pp 1–7

    Google Scholar 

  21. Alam S, Shuaib M, Samad A (2019) A collaborative study of intrusion detection and prevention techniques in cloud computing. In: Lecture notes in networks and systems, vol 55. Springer, pp 231–240. https://doi.org/10.1007/978-981-13-2324-9_23

  22. Shuaib M, Daud SM, Alam S, Khan WZ (2020) Blockchain-based framework for secure and reliable land registry system. TELKOMNIKA Telecommun Comput Electron Control 18(5):2560–2571. https://doi.org/10.12928/TELKOMNIKA.v18i5.15787

  23. Shuaib M, Daud SM, Alam S (2021) Self-sovereign identity framework development in compliance with self sovereign identity principles using components. Int J Mod Agric 10(2):3277–3296

    Google Scholar 

  24. Khan ZA, Khubrani MM, Alam S, Hui SJ, Wang Y Method for measuring the similarity of multiple metrological sequences in the key phenological phase of rice-based on dynamic time

    Google Scholar 

  25. Bharany S, Sharma S, Bhatia S, Rahmani MKI, Shuaib M, Lashari SA (2022) Energy efficient clustering protocol for FANETS using moth flame optimization. Sustainability 14(10):6159

    Article  Google Scholar 

  26. Abdus S et al (2018) Internet of vehicles (IoV) requirements, attacks and countermeasures. In: 2018 5th International conference on computing for sustainable global development, pp 4037–4040

    Google Scholar 

  27. Tripathi M (2021) Sentiment analysis of Nepali COVID19 tweets using NB SVM and LSTM. J Artif Intell 3(03):151–168

    Google Scholar 

  28. Kotzias D, Denil M, De Freitas N, Smyth P (2015) From group to individual labels using deep features. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 597–606

    Google Scholar 

  29. Singh P, Singh S, Sohal M, Dwivedi YK, Kahlon KS, Sawhney RS (2020) Psychological fear and anxiety caused by COVID-19: insights from twitter analytics. Asian J Psychiatr 54:102280

    Article  Google Scholar 

  30. Brahmi N, Singh P, Sohal M, Sawhney RS (2020) Psychological trauma among the healthcare professionals dealing with COVID-19. Asian J Psychiatr 54:102241

    Article  Google Scholar 

  31. Pandian AP (2021) Performance evaluation and comparison using deep learning techniques in sentiment analysis. J Soft Comput Paradig 3(02):123–134

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, India

    Salil Bharany

  2. Department of Computer Science, Jazan University, Jazan, Saudi Arabia

    Shadab Alam & Mohammed Shuaib

  3. Department of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India

    Bhanu Talwar

Authors
  1. Salil Bharany
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shadab Alam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammed Shuaib
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bhanu Talwar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shadab Alam .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, GITAM University, Bangalore, Karnataka, India

    I. Jeena Jacob

  2. Department of Mathematics and Computer Science, Ashland University, River Forest, IL, USA

    Selvanayaki Kolandapalayam Shanmugam

  3. Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, Ukraine

    Ivan Izonin

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bharany, S., Alam, S., Shuaib, M., Talwar, B. (2023). Sentiment Analysis of Twitter Data for COVID-19 Posts. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Izonin, I. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-6004-8_37

Download citation

Publish with us

Policies and ethics

Search

Navigation