Abstract
Today we are in the era of Big Data, and users’ opinions on certain topics are flooding the web. By analysing tweets collected from communities, social media or even messaging systems, it is possible to obtain some interesting results. This phenomenon is important for knowledge workers, who analyse textual content published on the Internet to obtain information that can be used in decision-making. While the content produced on social networks is invaluable for knowledge extraction, the very process of extracting meaningful knowledge is not trivial and involves data and text mining methodologies and techniques that are by no means simple. The following work proposes a batch analysis of information drawn from Tweets by examining texts of news downloaded at different times of the day related to energy communities, using techniques of Sentiment Analysis, Natural Language Processing, Machine Learning and Big Data Analytics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
- 10.
- 11.
- 12.
References
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Di Martino, B., et al.: A big data pipeline and machine learning for a uniform semantic representation of structured data and documents from information systems of Italian ministry of justice. Int. J. Grid High Perform. Comput. (IJGHPC), 14 (2021)
Di Martino, B., Branco, D., Cante, L.C., Venticinque, S., Scholten, R., Bosma, B.: Semantic and knowledge based support to business model evaluation to stimulate green behaviour of electric vehicles’ drivers and energy prosumers. J. Am. Intell. Human. Comput. 1–23 (2021). https://doi.org/10.1007/s12652-021-03243-4
Di Martino, B., Cascone, D., Colucci Cante, L., Esposito, A.: Semantic representation and rule based patterns discovery and verification in eProcurement business processes for eGovernment. In: Barolli, L., Yim, K., Enokido, T. (eds.) CISIS 2021. LNNS, vol. 278, pp. 667–676. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79725-6_67
Di Martino, B., Colucci Cante, L., Esposito, A., Lupi, P., Orlando, M.: Supporting the optimization of temporal key performance indicators of Italian courts of justice with OLAP techniques. In: Barolli, L., Yim, K., Enokido, T. (eds.) CISIS 2021. LNNS, vol. 278, pp. 646–656. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79725-6_65
Di Martino, B., Colucci Cante, L., Graziano, M., Enrich Sard, R.: 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.) CISIS 2020. AISC, vol. 1194, pp. 510–519. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-50454-0_53
Di Martino, B., Esposito, A., Cante, L.C.: Multi agents simulation of justice trials to support control management and reduction of civil trials duration. J. Am. Intell. Human. Comput. 1–13 (2021). https://doi.org/10.1007/s12652-021-03490-5
D’Agostino, G., Tofani, A., Di Martino, B., Marulli, F.: Toward ECListener: an unsurpervised intelligent system to monitor energy communities. In: Barolli, L., Yim, K., Enokido, T. (eds.) CISIS 2021. LNNS, vol. 278, pp. 616–626. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79725-6_62
Esuli, A., Sebastiani, F.: PageRanking WordNet Synsets: an application to opinion mining. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp. 424–431 (2007)
Loria, S., et al.: TextBlob documentation. Release 0.15, 2:269 (2018)
Di Martino, B., Cante, L.C., Esposito, A., Lupi, P., Orlando, M.: Temporal outlier analysis of online civil trial cases based on graph and process mining techniques. Int. J. Big Data Intell. 8(1), 31–46 (2021)
Di Martino, B., Colucci Cante, L., Venticinque, S.: An ontology framework for evaluating E-mobility innovation. In: Barolli, L., Poniszewska-Maranda, A., Enokido, T. (eds.) CISIS 2020. AISC, vol. 1194, pp. 520–529. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-50454-0_54
Di Martino, B., Marulli, F., Graziano, M., Lupi, P.: PrettyTags: an open-source tool for easy and customizable textual MultiLevel semantic annotations. In: Barolli, L., Yim, K., Enokido, T. (eds.) CISIS 2021. LNNS, vol. 278, pp. 636–645. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79725-6_64
Zhang, Y., Abbas, M., Iqbal, W.: Perceptions of GHG emissions and renewable energy sources in Europe, Australia and the USA. Environ. Sci. Poll. Res. 29(4), 5971–5987 (2022)
Acknowledgements
The work described in this paper has been supported by the Project VALERE “SSCeGov - Semantic, Secure and Law Compliant e-Government Processes”. The research described in this work was also performed in collaboration with ENEA and supported by the Project 1.7 “Technologies for the efficient penetration of the electric vector in the final uses” within the “Electrical System Research” Programme Agreements 19–21.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Di Martino, B. et al. (2022). Machine Learning, Big Data Analytics and Natural Language Processing Techniques with Application to Social Media Analysis for Energy Communities. In: Barolli, L. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2022. Lecture Notes in Networks and Systems, vol 497. Springer, Cham. https://doi.org/10.1007/978-3-031-08812-4_41
Download citation
DOI: https://doi.org/10.1007/978-3-031-08812-4_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-08811-7
Online ISBN: 978-3-031-08812-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)