Abstract
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.
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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)
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)
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)
Kumar, A., Abraham, A.: Opinion mining to assist user acceptance testing for open-beta versions. J. Inf. Assur. Secur. 12(4), 46–153 (2017)
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)
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)
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)
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)
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)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retriev. 2(1–2), 1–135 (2008)
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)
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)
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)
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)
Reyes, A., Rosso, P.: Making objective decisions from subjective data: detecting irony in customer reviews. Decis. Support Syst. 53(4), 754–760 (2012)
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)
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)
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)
Kumar, A., Jaiswal, A.: Swarm intelligence based optimal feature selection for enhanced predictive sentiment accuracy on twitter. Multimed. Tools App;. 2019, 1–25 (2019)
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)
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). https://doi.org/10.1002/cpe.5107
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)
Wu, F., Song, Y., Huang, Y.: Microblog sentiment classification with heterogeneous sentiment knowledge. Inf. Sci. (Ny) 373, 149–164 (2016)
Xu, S.: Bayesian Naïve Bayes classifiers to text classification. J. Inf. Sci. 44(1), 48–59 (2018)
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)
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)
de Vlaming, R., Groenen, P.J.: The current and future use of ridge regression for prediction in quantitative genetics. BioMed Res. Int. (2015)
Wang, G., et al.: Sentiment classification: the contribution of ensemble learning. Decis. Support Syst. (2013). https://doi.org/10.1016/j.dss.2013.08.002
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)
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)
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)
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)
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)
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Kumar, A., Jaiswal, A. Scalable intelligent data-driven decision making for cognitive cities. Energy Syst 13, 581–599 (2022). https://doi.org/10.1007/s12667-019-00369-5
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DOI: https://doi.org/10.1007/s12667-019-00369-5