Skip to main content

Analysis of Sentimental Behaviour over Social Data Using Machine Learning Algorithms

Part of the Lecture Notes in Computer Science book series (LNAI,volume 12798)

Abstract

A person’s sentiment is rigorously influenced by his emotional feelings which is evoked from every single incident, occurring every day in his surroundings. In this case, the decision that he makes is greatly affected by his sentiment rather than facts. Sentimental behavior can be applied to many applications in health, business, education, etc. This paper proposes and develops a sentimental behavior based on machine learning algorithms for pre-processing feature selection, and classification that helps identify, extract, quantify the feelings to twitter social dataset. Based on the Sentimental behavior, an analytical prediction has been developed that can be used to understand the behavior of the customers or users. Conducting the testing process for three state-of-the-art algorithms, the unique methodology is devised based on these proposed algorithms (support vector machine (SVM), logistic regression (LR), and XGboost), our extensive experiments show that our approach has high accuracy. Based on the testing results, the accurate sentimental behavior detection algorithm is identified and recommended to be used for textual data in the future.

Keywords

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

References

  1. Sahayak, V., Shete, V., Pathan, A.: Sentiment analysis on twitter data. (IJIRAE) ISSN: 2349–2163, January 2015

    Google Scholar 

  2. Bouazizi, M., Ohtsuki, T.: Sentiment analysis: from binary to multi-class classification. In: IEEE ICC 2016 SAC Social Networking, ISBN 978-1-4799-6664-6 (2016)

    Google Scholar 

  3. Balas, V.E., et al. (eds.): Internet of Things and Big Data Analytics for Smart Generation, vol. 154, p. 3. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-030-04203-5

    Book  Google Scholar 

  4. Mamgain, N., Mehta, E., Mittal, A., Bhatt, G.: Sentiment analysis of top colleges in India using Twitter data, ISBN -978-1-5090-0082-1. IEEE (2016)

    Google Scholar 

  5. https://businessmodelsinc.medium.com/exploring-big-data-business-models-the-winning-value-propositions-behind-them-f7b182458d98

  6. Halima Banu, S., Chitrakala, S.: Trending topic analysis using novel sub topic detection model, ISBN- 978-1-4673-9745-2. IEEE (2016)

    Google Scholar 

  7. Moghaddam, S., Ester, M.: ILDA: interdependent LDA model for learning latent aspects and their ratings from online product reviews. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (2011)

    Google Scholar 

  8. Khan, A., Baharudin, B., Khan, K.: Sentiment classification from online customer reviews using lexical contextual sentence structure. In: Mohamad Zain, J., Wan Mohd, W.M., El-Qawasmeh, E. (eds.) ICSECS 2011. CCIS, vol. 179, pp. 317–331. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22170-5_28. ISBN 978-3-642-22170-5

    Chapter  Google Scholar 

  9. Dietmar, G., Markus, Z., Günther, F., Matthias, F.: Classification of customer reviews based on sentiment analysis. In: Fuchs, M., Ricci, F., Cantoni, L. (eds.) Information and Communication Technologies in Tourism, pp. 460–470. Springer, Vienna (2012). https://doi.org/10.1007/978-3-7091-1142-0_40. ISBN 978-3-7091-1142-0

    Chapter  Google Scholar 

  10. Bermingham, A., Smeaton, A.F.: Classifying sentiment in microblogs: is brevity an advantage is brevity an advantage? (2010)

    Google Scholar 

  11. Aceto, G., Ciuonzo, D., Montieri, A., Pescapé, A.: Multi-classification approaches for classifying mobile app traffic. J. Netw. Comput. Appl. 103, 131–145 (2018)

    Article  Google Scholar 

  12. Montieri, A., Ciuonzo, D., Aceto, G., Pescape, A.: Anonymity services Tor, I2P, JonDonym: classifying in the dark (web). IEEE Trans. Dependable Secure Comput. 17, 662–675 (2018)

    Article  Google Scholar 

  13. Montieri, A., Ciuonzo, D., Bovenzi, G., Persico, V., Pescapé, A.: A dive into the dark web: hierarchical traffic classification of anonymity tools. IEEE Trans. Netw. Sci. Eng. 7, 1043–1054 (2019)

    Article  Google Scholar 

  14. Devi, M.I., Rajaram, R., Selvakuberan, K.: Generating best features for web page classification. Webology, 5(1), Article 52 (2008)

    Google Scholar 

  15. Gamon, M.: Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis (2014)

    Google Scholar 

  16. Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Technical report, Stanford (2009)

    Google Scholar 

  17. Blei, D., Lafferty, J.: Topic models. In: Text Mining: Classification, Clustering, and Applications, p. 10 (2015)

    Google Scholar 

  18. Pouliquen, B., Steinberger, R., Best, C.: Automatic detection of quotations in multilingual news (2017)

    Google Scholar 

  19. Uddin, M.F., Rizvi, S., Razaque, A.: Proposing logical table constructs for enhanced machine learning process. IEEE Access 6, 47751–47769 (2018)

    Article  Google Scholar 

  20. Almi’ani, M., Ghazleh, A.A., Al-Rahayfeh, A., Razaque, A.: Intelligent intrusion detection system using clustered self organized map. In: 2018 Fifth International Conference on Software Defined Systems (SDS), pp. 138–144. IEEE (2018)

    Google Scholar 

  21. Kolk, R., Razaque, A.: Scalable and energy efficient computer vision for text translation. In: 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT), pp. 1–6. IEEE (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohamed Baza .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Razaque, A., Amsaad, F., Halder, D., Baza, M., Aboshgifa, A., Bhatia, S. (2021). Analysis of Sentimental Behaviour over Social Data Using Machine Learning Algorithms. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12798. Springer, Cham. https://doi.org/10.1007/978-3-030-79457-6_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-79457-6_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-79456-9

  • Online ISBN: 978-3-030-79457-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics