Abstract
Sentiment Analysis techniques have been largely applied to Tweets, newsgroups and Social Networks in general, with several applications in sociological studies. Users tend to comment and express their opinions much more genuinely on Social Networks, as if their natural filters were somehow lifted. In particular, complaints regarding malfunctions of specific services are often filed in form of public comments or Tweets, on the official accounts of the Service providers. In some cases, people just express dissatisfaction regarding services on their own accounts, and use hashtags to better identify the specific topic they are referring to. In this paper, a framework for the analysis of Tweets is proposed, with the specific objective to identify malfunctioning of essential services, such as water, electrical, gas or public illumination. Since the number of comments and Tweets to analyse is considerable, a microservices based architecture, with Docker containers and Kafka queues, has been created. This allows to define a scalable and parallelizable architecture, whose characteristics can be adapted to the number of Tweets to be analysed, which are in turn treated as a continuous data streaming.
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References
Bernstein, D.: Containers and cloud: from LXC to Docker to Kubernetes. IEEE Cloud Comput. 1(3), 81–84 (2014)
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.) Complex, Intelligent and Software Intensive Systems. CISIS 2020. Advances in Intelligent Systems and Computing, vol. 1194, pp. 510–519. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50454-0_53
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) (2021, in press)
Di Martino, B., Venticinque, S., Esposito, A., D’Angelo, S.: A methodology based on computational patterns for offloading of big data applications on cloud-edge platforms. Future Internet 12(2), 28 (2020)
Grinberg, M.: Flask Web Development: Developing Web Applications with Python. O’Reilly Media, Inc. (2018)
Jaramillo, D., Nguyen, D.V., Smart, R.: Leveraging microservices architecture by using Docker technology. In: SoutheastCon 2016, pp. 1–5. IEEE (2016)
Thein, K.M.M.: Apache Kafka: next generation distributed messaging system. Int. J. Sci. Eng. Technol. Res. 3(47), 9478–9483 (2014)
Acknowledgments
This project has received funding from the European Union’s Horizon 2020 research and innovation program through the NGI ONTOCHAIN program under cascade funding agreement No 957338.
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Di Martino, B., Bombace, V., D’Angelo, S., Esposito, A. (2022). A Microservices Based Architecture for the Sentiment Analysis of Tweets. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 451. Springer, Cham. https://doi.org/10.1007/978-3-030-99619-2_12
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DOI: https://doi.org/10.1007/978-3-030-99619-2_12
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