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Predictive Analytics Using Social Big Data and Machine Learning

In: Social Big Data Analytics

Author

Listed:
  • Bilal Abu-Salih

    (The University of Jordan)

  • Pornpit Wongthongtham

    (The University of Western Australia)

  • Dengya Zhu

    (Curtin University)

  • Kit Yan Chan

    (Curtin University)

  • Amit Rudra

    (Curtin University)

Abstract

The ever-increase in the quality and quantity of data generated from day-to-day businesses operations in conjunction with the continuously imported related social data have made the traditional statistical approaches inadequate to tackle such data floods. This has dictated researchers to design and develop advance and sophisticated analytics that can be incorporated to gain valuable insights that benefit business domain. This chapter sheds the light on core aspects that lay the foundations for social big data analytics. In particular, the significant of predictive analytics in the context of SBD is discussed fortified with presenting a framework for SBD predictive analytics. Then, various predictive analytical algorithms are introduced with their usage in several important application and top-tier tools and APIs. A case study on using predictive analytics to social data is provided supports with experiments to substantiate significance and utility of predictive analytics.

Suggested Citation

  • Bilal Abu-Salih & Pornpit Wongthongtham & Dengya Zhu & Kit Yan Chan & Amit Rudra, 2021. "Predictive Analytics Using Social Big Data and Machine Learning," Springer Books, in: Social Big Data Analytics, chapter 0, pages 113-143, Springer.
    • Handle: RePEc:spr:sprchp:978-981-33-6652-7_5
    DOI: 10.1007/978-981-33-6652-7_5
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    Cited by:

    1. Pornpit Wongthongtham & Bilal Abu-Salih & Jeff Huang & Hemixa Patel & Komsun Siripun, 2023. "A Multi-Criteria Analysis Approach to Identify Flood Risk Asset Damage Hotspots in Western Australia," Sustainability, MDPI, vol. 15(7), pages 1-30, March.

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