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Tweets Analysis with Big Data Technology and Machine Learning to Evaluate Smart and Sustainable Urban Mobility Actions in Barcelona

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Complex, Intelligent and Software Intensive Systems (CISIS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1194))

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Abstract

Social media can become a valuable data source to gather user opinions on topics of interest. Setting the focus on Twitter as one of the most popular social media channels to share opinions, three challenges have been identified in this work: to obtain users’ sentiment: to classify the topics of interest, to decide whether the opinion is positive or negative by applying sentiment analysis to natural language in written form, and to handle in an efficient manner the huge volume of data generated by Twitter. This paper shows how machine learning and big data technologies have been applied to classify user opinions on electromobility in Barcelona. Supervised and unsupervised approaches have been compared in terms of accuracy and a big data framework based on Spark has been implemented for real time processing combined with batch modelling. The results obtained show potential to apply them as a complementary mechanism to surveys.

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Notes

  1. 1.

    https://www.greencharge2020.eu/.

References

  1. Engels, D., Van Den Bergh, G., Breemersch, T.: Refined CIVITAS process and impact evaluation framework. CIVITAS SATELLITE project, August 2017

    Google Scholar 

  2. Livingston, F.: Implementation of Breiman’s random forest machine learning algorithm. ECE591Q Machine Learning Journal Paper. Fall (2005)

    Google Scholar 

  3. MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)

    Google Scholar 

  4. Korkomaz, M., Guney, S., Yigiter, S.Y.: The importance of logistic regression implementations in the Turkish livestock sector and logistic regression implementations/fields. J. Agric. Fac. HR.U. 16(2), 25–36 (2012)

    Google Scholar 

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Acknowledgments

This publication has been partially supported by GreenCharge and the use case here presented has been inspired in the project impact evaluation.

The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 769016.

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Correspondence to Regina Enrich Sard .

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Di Martino, B., Colucci Cante, L., Graziano, M., Enrich Sard, R. (2021). Tweets Analysis with Big Data Technology and Machine Learning to Evaluate Smart and Sustainable Urban Mobility Actions in Barcelona. In: Barolli, L., Poniszewska-Maranda, A., Enokido, T. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2020. Advances in Intelligent Systems and Computing, vol 1194. Springer, Cham. https://doi.org/10.1007/978-3-030-50454-0_53

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