Skip to main content

Ramification of Sentiments on Robot-Based Smart Agriculture: An Analysis Using Real-Time Tweets

  • Conference paper
  • First Online:
Advanced Information Networking and Applications (AINA 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 655))

  • 593 Accesses

Abstract

Social users and their sentiments on robot-based agriculture is an advanced area of research as demands for robots are increasing vividly in smart agriculture. Based on available studies, which usually depends on tweets, it helps the users to realize opinion on various aspects. Therefore, in this research work, a framework is designed to study users’ sentiments, including their contextual behavior in terms of sentiment variations. The results show that the users have a positive attitude toward smart agriculture based on robots. Still, at the same time, they have a biased opinion also for various robot terms. Significant tweets based on the adoption of robots in agriculture are extracted in real-time using various event-based terms such as security, adoption rate, unemployment, and safety. Thus, this work will benefit the various business agencies, manufacturers, and technology-based organizations in understanding users’ attitudes toward adopting robots in smart agriculture.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Singh, T., Kumari, M.: Role of text pre-processing in Twitter sentiment analysis. Procedia Comput. Sci. 89, 549–554 (2016)

    Article  Google Scholar 

  2. Singh, T., Kumari, M.: Burst: real-time events burst detection in the social text stream. J. Supercomput. 77(10), 11228–11256 (2021)

    Article  Google Scholar 

  3. Singh, T., Kumari, M., Gupta, D.S.: Real-time event detection and classification in social text stream using embedding. Cluster (2022)

    Google Scholar 

  4. Kaliyar, R.K., Goswami, A., Narang, P.: FakeBERT: fake news detection in social media with a BERT-based deep learning approach. Multimedia Tools Appl. 80(8), 11765–11788 (2021). https://doi.org/10.1007/s11042-020-10183-2

    Article  Google Scholar 

  5. Choi, D., Oh, H., Chun, S., Kwon, T., Han, J.: Preventing rumor spread with deep learning. Expert Syst. Appl. 197, 116688 (2022)

    Article  Google Scholar 

  6. Hasan, M., Uddin, K.N.W., Sayeed, A., Tasneem, T.: Smart agriculture robotic system based on internet of things to boost crop production. In: 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), pp. 157–162. IEEE (2021)

    Google Scholar 

  7. Bai, Y., Mao, S., Zhou, J., Zhang, B.: Clustered tomato detection and picking point location using machine learning-aided image analysis for automatic robotic harvesting. Precis. Agric. 1–17 (2022)

    Google Scholar 

  8. Friha, O., Ferrag, M.A., Shu, L., Maglaras, L., Wang, X.: Internet of things for the future of smart agriculture: a comprehensive survey of emerging technologies. IEEE/CAA J. Autom. Sinica 8(4), 718–752 (2021)

    Article  Google Scholar 

  9. Lukasik, M., Srijith, P.K., Vu, D., Bontcheva, K., Zubiaga, A., Cohn, T.: Hawkes processes for continuous time sequence classification: an application to rumour stance classification in twitter. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 393–398 (2016)

    Google Scholar 

  10. Rane, A., Kumar, A.: Sentiment classification system of Twitter data for US airline service analysis. In: 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), vol. 1, pp. 769–773. IEEE (2018)

    Google Scholar 

  11. Pathan, M., Patel, N., Yagnik, H., Shah, M.: Artificial cognition for applications in smart agriculture: a comprehensive review. Artif. Intell. Agric. 4, 81–95 (2020)

    Google Scholar 

  12. Vougioukas, S.: Annual review of control, robotics, and autonomous systems. Agric. Robot. 2(1), 365–392 (2019)

    Google Scholar 

  13. Zhang, B., Xie, Y., Zhou, J., Wang, K., Zhang, Z.: State-of-the-art robotic grippers, grasping and control strategies, as well as their applications in agricultural robots: a review. Comput. Electron. Agric. 177, 105694 (2020)

    Article  Google Scholar 

  14. Nazir, F., Ghazanfar, M.A., Maqsood, M., Aadil, F., Rho, S., Mehmood, I.: Social media signal detection using tweets volume, hashtag, and sentiment analysis. Multimedia Tools Appl. 78(3), 3553–3586 (2019)

    Article  Google Scholar 

  15. Kolajo, T., Daramola, O., Adebiyi, A.A.: Real-time event detection in social media streams through semantic analysis of noisy terms. J. Big Data 9(1), 1–36 (2022)

    Article  Google Scholar 

  16. McMinn, A.J., Jose, J.M.: Real-time entity-based event detection for Twitter. In: Mothe, J., et al. (eds.) CLEF 2015. LNCS, vol. 9283, pp. 65–77. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24027-5_6

    Chapter  Google Scholar 

Download references

Acknowledgment

The authors thank the anonymous referees for their valuable comments that were helpful in improving the paper. The third author was in part supported by a research grant from Google.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Amar Nath .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Singh, T., Nath, A., Niyogi, R. (2023). Ramification of Sentiments on Robot-Based Smart Agriculture: An Analysis Using Real-Time Tweets. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2023. Lecture Notes in Networks and Systems, vol 655. Springer, Cham. https://doi.org/10.1007/978-3-031-28694-0_20

Download citation

Publish with us

Policies and ethics