Scalable intelligent data-driven decision making for cognitive cities

  • Akshi Kumar
  • Arunima JaiswalEmail author
Original Paper


Advancement of Web 2.0 has completely revolutionized the way people communicate and exchange information among them using social media such as Twitter etc. People are using smart technology based solutions (including IoT’s, sensors etc.) at an unprecedented scale for enhancing their quality of the life and building a smart city. These social sensors etc. are huge source of sentiment-rich data that needs to be processed and comprehended well in order to facilitate enhanced and smart decision making by the citizens of the smart city for any product, service or policy etc. The objective of this research is to leverage deeper insights of application of intelligent computation techniques for sentiment analysis of cognitive cities using user-generated data to improve the urban ecosystem. Sentiment analysis is quite imperative and serve as info-foundation for smart cities as they have the ability to harness the opinions or sentiments accurately based on the computation technology applied. The results are evaluated and analyzed on Twitter datasets that are constructed by the students of the Stanford University. It is observed that the alliance of big data and social media analytics using intelligent sentiment computation has helped in making citizens smart for taking smart decisions and eventually building a smart city.


Web 2.0 Sentiment analysis Social media Crowd-sensing Cognitive city Smart decision making 


Compliance with ethical standards

Conflict of interest

There is no conflict of interests.


  1. 1.
    Somov, A., Dupont, C., Giaffreda, R.: Supporting smart-city mobility with cognitive Internet of Things. In: Future Network and Mobile Summit, IEEE, pp. 1–10 (2013)Google Scholar
  2. 2.
    Mohammadi, M., Al-Fuqaha, A.: Enabling cognitive smart cities using big data and machine learning: approaches and challenges. IEEE Commun Mag 56(2), 94–101 (2018)CrossRefGoogle Scholar
  3. 3.
    Wu, Q., Ding, G., Xu, Y., Feng, S., Du, Z., Wang, J., Long, K.: Cognitive internet of things: a new paradigm beyond connection. IEEE Internet of Things J. 1(2), 129–143 (2014)CrossRefGoogle Scholar
  4. 4.
    Kumar, A., Abraham, A.: Opinion mining to assist user acceptance testing for open-beta versions. J. Inf. Assur. Secur. 12(4), 46–153 (2017)Google Scholar
  5. 5.
    Vlacheas, P., Giaffreda, R., Stavroulaki, V., Kelaidonis, D., Foteinos, V., Poulios, G., Moessner, K.: Enabling smart cities through a cognitive management framework for the internet of things. IEEE Commun Mag 51(6), 102–111 (2013)CrossRefGoogle Scholar
  6. 6.
    Khan, Z., Anjum, A., Soomro, K., Tahir, M.A.: Towards cloud based big data analytics for smart future cities. J. Cloud Comput. 4, 1 (2015)CrossRefGoogle Scholar
  7. 7.
    Kumar, A., Jaiswal, A., Garg, S., Verma, S., Kumar, S.: Sentiment analysis using cuckoo search for optimized feature selection on kaggle tweets. Int. J. Inf. Retriev. Res. 9, 1–15 (2019)Google Scholar
  8. 8.
    Ahmed, K.B., Radenski, A., Bouhorma, M., Ahmed, M.B.: Sentiment analysis for smart cities: state of the art and opportunities. In: Proceedings on the International Conference on Internet Computing, pp. 55. ICOMP. The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp) (2016)Google Scholar
  9. 9.
    Kumar, A. Joshi, A.: Ontology driven sentiment analysis on social web for government intelligence. In: Proceedings of the Special Collection on eGovernment Innovations in India, pp. 134–139. ACM (2017)Google Scholar
  10. 10.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retriev. 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  11. 11.
    Kumar, A., Dogra, P., Dabas, V.: Emotion analysis of Twitter using opinion mining. In: Contemporary Computing, 8th International Conference on IC3, pp. 285–290. IEEE (2015)Google Scholar
  12. 12.
    Kumar, A., Sebastian, T.M.: Machine learning assisted sentiment analysis. In: Proceedings of International Conference on Computer Science and Engineering, pp. 123–130. ICCSE (2012)Google Scholar
  13. 13.
    Massobrio, R., Nesmachnow, S., Tchernykh, A.N., Avetisyan, A.I., Radchenko, G.I.: Towards a cloud computing paradigm for big data analysis in smart cities. Proc. Inst. Syst. Program. RAS 28(6), 121–140 (2012)CrossRefGoogle Scholar
  14. 14.
    Kumar, A., Sebastian, T.M.: Sentiment analysis: a perspective on its past, present and future. Int. J. Intell. Syst. Appl. 4(10), 1–14 (2012)Google Scholar
  15. 15.
    Reyes, A., Rosso, P.: Making objective decisions from subjective data: detecting irony in customer reviews. Decis. Support Syst. 53(4), 754–760 (2012)CrossRefGoogle Scholar
  16. 16.
    Xhafa, F., Barolli, L., Barolli, A., Papajorgji, P.: Modeling and Processing for Next-Generation Big-Data Technologies: With Applications and Case Studies, 4th edn. Springer, Berlin (2014)zbMATHGoogle Scholar
  17. 17.
    Durán-Sánchez, A., del Río, M.D.L.C., Sereno-Ramírez, A., Bredis, K.: Sustainability and quality of life in smart cities: analysis of scientific production. Sustainable Smart Cities, pp. 159–181. Springer, Cham (2017)CrossRefGoogle Scholar
  18. 18.
    Schaffers, H., Komninos, N., Pallot, M., Trousse, B., Nilsson, M., Oliveira, A.: Smart cities and the future internet: towards cooperation frameworks for open innovation. The Future Internet Assembly, pp. 431–446. Springer, Berlin (2011)CrossRefGoogle Scholar
  19. 19.
    Kumar, A., Jaiswal, A.: Swarm intelligence based optimal feature selection for enhanced predictive sentiment accuracy on twitter. Multimed. Tools App;. 2019, 1–25 (2019)Google Scholar
  20. 20.
    Lak, P., Turetken, O.: The impact of sentiment analysis output on decision outcomes: an empirical evaluation. AIS Trans. Hum. Comput. Interact. 9(1), 1–22 (2017)CrossRefGoogle Scholar
  21. 21.
    Kumar, A., Jaiswal, A.: Systematic Literature Review of Sentiment Analysis on Twitter Using Soft Computing Techniques. Concurrency and Computation Practice and Experience. Wiley, New York (2019). CrossRefGoogle Scholar
  22. 22.
    Sulis, E., Farías, D.I.H., Rosso, P., Patti, V., Ruffo, G.: Figurative messages and affect in Twitter: differences between# irony,# sarcasm and# not. Knowl. Based Syst. 108, 132–143 (2017)CrossRefGoogle Scholar
  23. 23.
    Wu, F., Song, Y., Huang, Y.: Microblog sentiment classification with heterogeneous sentiment knowledge. Inf. Sci. (Ny) 373, 149–164 (2016)CrossRefGoogle Scholar
  24. 24.
    Xu, S.: Bayesian Naïve Bayes classifiers to text classification. J. Inf. Sci. 44(1), 48–59 (2018)CrossRefGoogle Scholar
  25. 25.
    Altheneyan, A.S., Menai, M.E.B.: Naïve Bayes classifiers for authorship attribution of Arabic texts. J. King Saud Univ. Comput. Inf. Sci. 26(4), 473–484 (2014)Google Scholar
  26. 26.
    Grüning, M., Kropf, S.: A ridge classification method for high-dimensional observations. From Data and Information Analysis to Knowledge Engineering, pp. 684–691. Springer, Berlin (2006)CrossRefGoogle Scholar
  27. 27.
    de Vlaming, R., Groenen, P.J.: The current and future use of ridge regression for prediction in quantitative genetics. BioMed Res. Int. (2015)Google Scholar
  28. 28.
    Wang, G., et al.: Sentiment classification: the contribution of ensemble learning. Decis. Support Syst. (2013). CrossRefGoogle Scholar
  29. 29.
    Wang, N, Varghese B, Donnelly, P.D.: A machine learning analysis of Twitter sentiment to the sandy hook shootings. In: Proceedings of 12th International IEEE Conference on e-Science, USA, pp. 3–312 (2016)Google Scholar
  30. 30.
    Kumar, A., Jaiswal, A.: Empirical study of Twitter and Tumblr for sentiment analysis using soft computing techniques. Proc. World Congr. Eng. Comput. Sci. 1, 1–5 (2017)Google Scholar
  31. 31.
    Ghiassi, M., Lee, S.: A domain transferable lexicon set for Twitter sentiment analysis using a supervised machine learning approach. Expert Syst. Appl. 106, 197–216 (2018)CrossRefGoogle Scholar
  32. 32.
    Symeonidis, S., Effrosynidis, D., Arampatzis, A.: A comparative evaluation of pre-processing techniques and their interactions for twitter sentiment analysis. Expert Syst. Appl. 110, 298–310 (2018)CrossRefGoogle Scholar
  33. 33.
    Tan, W.K., Tan, C.H., Teo, H.H.: Consumer-based decision aid that explains which to buy: decision confirmation or overconfidence bias? Decis. Support Syst. 53(1), 127–141 (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringDelhi Technological UniversityDelhiIndia

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